Research

12 pages, 1842 KiB

Open AccessArticle

Deep Neural Network Regression to Assist Non-Invasive Diagnosis of Portal Hypertension

by Federico Baldisseri, Andrea Wrona, Danilo Menegatti, Antonio Pietrabissa, Stefano Battilotti, Claudia Califano, Andrea Cristofaro, Paolo Di Giamberardino, Francisco Facchinei, Laura Palagi, Alessandro Giuseppi and Francesco Delli Priscoli

Healthcare 2023, 11(18), 2603; https://doi.org/10.3390/healthcare11182603 - 21 Sep 2023

Viewed by 1079

Abstract

Portal hypertension is a complex medical condition characterized by elevated blood pressure in the portal venous system. The conventional diagnosis of such disease often involves invasive procedures such as liver biopsy, endoscopy, or imaging techniques with contrast agents, which can be uncomfortable for [...] Read more.

Portal hypertension is a complex medical condition characterized by elevated blood pressure in the portal venous system. The conventional diagnosis of such disease often involves invasive procedures such as liver biopsy, endoscopy, or imaging techniques with contrast agents, which can be uncomfortable for patients and carry inherent risks. This study presents a deep neural network method in support of the non-invasive diagnosis of portal hypertension in patients with chronic liver diseases. The proposed method utilizes readily available clinical data, thus eliminating the need for invasive procedures. A dataset composed of standard laboratory parameters is used to train and validate the deep neural network regressor. The experimental results exhibit reasonable performance in distinguishing patients with portal hypertension from healthy individuals. Such performances may be improved by using larger datasets of high quality. These findings suggest that deep neural networks can serve as useful auxiliary diagnostic tools, aiding healthcare professionals in making timely and accurate decisions for patients suspected of having portal hypertension. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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19 pages, 2275 KiB

Open AccessArticle

Convolutional Neural Networks to Assess Steno-Occlusive Disease Using Cerebrovascular Reactivity

by Yashesh Dasari, James Duffin, Ece Su Sayin, Harrison T. Levine, Julien Poublanc, Andrea E. Para, David J. Mikulis, Joseph A. Fisher, Olivia Sobczyk and Mir Behrad Khamesee

Healthcare 2023, 11(16), 2231; https://doi.org/10.3390/healthcare11162231 - 08 Aug 2023

Viewed by 1203

Abstract

Cerebrovascular Reactivity (CVR) is a provocative test used with Blood oxygenation level-dependent (BOLD) Magnetic Resonance Imaging (MRI) studies, where a vasoactive stimulus is applied and the corresponding changes in the cerebral blood flow (CBF) are measured. The most common clinical application is the [...] Read more.

Cerebrovascular Reactivity (CVR) is a provocative test used with Blood oxygenation level-dependent (BOLD) Magnetic Resonance Imaging (MRI) studies, where a vasoactive stimulus is applied and the corresponding changes in the cerebral blood flow (CBF) are measured. The most common clinical application is the assessment of cerebral perfusion insufficiency in patients with steno-occlusive disease (SOD). Globally, millions of people suffer from cerebrovascular diseases, and SOD is the most common cause of ischemic stroke. Therefore, CVR analyses can play a vital role in early diagnosis and guiding clinical treatment. This study develops a convolutional neural network (CNN)-based clinical decision support system to facilitate the screening of SOD patients by discriminating between healthy and unhealthy CVR maps. The networks were trained on a confidential CVR dataset with two classes: 68 healthy control subjects, and 163 SOD patients. This original dataset was distributed in a ratio of 80%-10%-10% for training, validation, and testing, respectively, and image augmentations were applied to the training and validation sets. Additionally, some popular pre-trained networks were imported and customized for the objective classification task to conduct transfer learning experiments. Results indicate that a customized CNN with a double-stacked convolution layer architecture produces the best results, consistent with expert clinical readings. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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16 pages, 716 KiB

Open AccessArticle

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

by Danilo Menegatti, Alessandro Giuseppi, Francesco Delli Priscoli, Antonio Pietrabissa, Alessandro Di Giorgio, Federico Baldisseri, Mattia Mattioni, Salvatore Monaco, Leonardo Lanari, Martina Panfili and Vincenzo Suraci

Healthcare 2023, 11(15), 2199; https://doi.org/10.3390/healthcare11152199 - 04 Aug 2023

Cited by 1 | Viewed by 1080

Abstract

Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number [...] Read more.

Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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19 pages, 907 KiB

Open AccessArticle

Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

by David Ricardo Castillo Salazar, Laura Lanzarini, Héctor Gómez, Saravana Prakash Thirumuruganandham and Dario Xavier Castillo Salazar

Healthcare 2023, 11(10), 1392; https://doi.org/10.3390/healthcare11101392 - 11 May 2023

Viewed by 1321

Abstract

The present work describes the development of a conceptual representation model of the domain of the theory of formal grammars and abstract machines through ontological modeling. The main goal is to develop an ontology capable of deriving new knowledge about the mood of [...] Read more.

The present work describes the development of a conceptual representation model of the domain of the theory of formal grammars and abstract machines through ontological modeling. The main goal is to develop an ontology capable of deriving new knowledge about the mood of an Alzheimer’s patient in the categories of wandering, nervous, depressed, disoriented or bored. The patients are from elderly care centers in Ambato Canton-Ecuador. The population consists of 147 individuals of both sexes, diagnosed with Alzheimer’s disease, with ages ranging from 75 to 89 years. The methods used are the taxonomic levels, the semantic categories and the ontological primitives. All these aspects allow the computational generation of an ontological structure, in addition to the use of the proprietary tool Pellet Reasoner as well as Apache NetBeans from Java for process completion. As a result, an ontological model is generated using its instances and Pellet Reasoner to identify the expected effect. It is noted that the ontologies come from the artificial intelligence domain. In this case, they are represented by aspects of real-world context that relate to common vocabularies for humans and applications working in a domain or area of interest. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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11 pages, 897 KiB

Open AccessArticle

A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients

by Domenica Antonia Bavaro, Annarita Fanizzi, Serena Iacovelli, Samantha Bove, Maria Colomba Comes, Cristian Cristofaro, Daniela Cutrignelli, Valerio De Santis, Annalisa Nardone, Fulvia Lagattolla, Alessandro Rizzo, Cosmo Maurizio Ressa and Raffaella Massafra

Healthcare 2023, 11(7), 1042; https://doi.org/10.3390/healthcare11071042 - 05 Apr 2023

Cited by 1 | Viewed by 1653

Abstract

In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent [...] Read more.

In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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13 pages, 435 KiB

Open AccessArticle

An ECG Classification Method Based on Multi-Task Learning and CoT Attention Mechanism

by Quancheng Geng, Hui Liu, Tianlei Gao, Rensong Liu, Chao Chen, Qing Zhu and Minglei Shu

Healthcare 2023, 11(7), 1000; https://doi.org/10.3390/healthcare11071000 - 31 Mar 2023

Cited by 2 | Viewed by 1874

Abstract

Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot [...] Read more.

Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot research topic. In this paper, we propose a new multi-task deep neural network, which includes a shared low-level feature extraction module (i.e., SE-ResNet) and a task-specific classification module. Contextual Transformer (CoT) block is introduced in the classification module to dynamically model the local and global information of ECG feature sequence. The proposed method was evaluated on public CPSC2018 and PTB-XL datasets and achieved an average F1 score of 0.827 on the CPSC2018 dataset and an average F1 score of 0.833 on the PTB-XL dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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16 pages, 3003 KiB

Open AccessArticle

Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes

by Rashid M. Ansari, Mark F. Harris, Hassan Hosseinzadeh and Nicholas Zwar

Healthcare 2023, 11(6), 903; https://doi.org/10.3390/healthcare11060903 - 21 Mar 2023

Cited by 2 | Viewed by 2661

Abstract

The use of Artificial intelligence in healthcare has evolved substantially in recent years. In medical diagnosis, Artificial intelligence algorithms are used to forecast or diagnose a variety of life-threatening illnesses, including breast cancer, diabetes, heart disease, etc. The main objective of this study [...] Read more.

The use of Artificial intelligence in healthcare has evolved substantially in recent years. In medical diagnosis, Artificial intelligence algorithms are used to forecast or diagnose a variety of life-threatening illnesses, including breast cancer, diabetes, heart disease, etc. The main objective of this study is to assess self-management practices among patients with type 2 diabetes in rural areas of Pakistan using Artificial intelligence and machine learning algorithms. Of particular note is the assessment of the factors associated with poor self-management activities, such as non-adhering to medications, poor eating habits, lack of physical activities, and poor glycemic control (HbA1c %). The sample of 200 participants was purposefully recruited from the medical clinics in rural areas of Pakistan. The artificial neural network algorithm and logistic regression classification algorithms were used to assess diabetes self-management activities. The diabetes dataset was split 80:20 between training and testing; 80% (160) instances were used for training purposes and 20% (40) instances were used for testing purposes, while the algorithms’ overall performance was measured using a confusion matrix. The current study found that self-management efforts and glycemic control were poor among diabetes patients in rural areas of Pakistan. The logistic regression model performance was evaluated based on the confusion matrix. The accuracy of the training set was 98%, while the test set’s accuracy was 97.5%; each set had a recall rate of 79% and 75%, respectively. The output of the confusion matrix showed that only 11 out of 200 patients were correctly assessed/classified as meeting diabetes self-management targets based on the values of HbA1c < 7%. We added a wide range of neurons (32 to 128) in the hidden layers to train the artificial neural network models. The results showed that the model with three hidden layers and Adam’s optimisation function achieved 98% accuracy on the validation set. This study has assessed the factors associated with poor self-management activities among patients with type 2 diabetes in rural areas of Pakistan. The use of a wide range of neurons in the hidden layers to train the artificial neural network models improved outcomes, confirming the model’s effectiveness and efficiency in assessing diabetes self-management activities from the required data attributes. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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16 pages, 820 KiB

Open AccessArticle

On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories

by Abdelghani Moussaid, Nabila Zrira, Ibtissam Benmiloud, Zineb Farahat, Youssef Karmoun, Yasmine Benzidia, Soumaya Mouline, Bahia El Abdi, Jamal Eddine Bourkadi and Nabil Ngote

Healthcare 2023, 11(5), 662; https://doi.org/10.3390/healthcare11050662 - 24 Feb 2023

Cited by 8 | Viewed by 1722

Abstract

The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been [...] Read more.

The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to the three classes mentioned above for both imaging modalities: radiography at 99.81% and CT at 99.88%. This work implements an accurate computer-aided system for the analysis of radiographic and CT medical images. The results of the classification are promising and will certainly improve the diagnosis and decision making of lung diseases that keep appearing over time. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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18 pages, 3539 KiB

Open AccessArticle

A Model to Predict Heartbeat Rate Using Deep Learning Algorithms

by Ahmed Alsheikhy, Yahia F. Said, Tawfeeq Shawly and Husam Lahza

Healthcare 2023, 11(3), 330; https://doi.org/10.3390/healthcare11030330 - 22 Jan 2023

Cited by 1 | Viewed by 1486

Abstract

ECG provides critical information in a waveform about the heart’s condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various [...] Read more.

ECG provides critical information in a waveform about the heart’s condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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14 pages, 2493 KiB

Open AccessArticle

Sharing Health Information Using a Blockchain

by Luis B. Elvas, Carlos Serrão and Joao C. Ferreira

Healthcare 2023, 11(2), 170; https://doi.org/10.3390/healthcare11020170 - 05 Jan 2023

Cited by 6 | Viewed by 2076

Abstract

Data sharing in the health sector represents a big problem due to privacy and security issues. Health data have tremendous value for organisations and criminals. The European Commission has classified health data as a unique resource owing to their ability to enable both [...] Read more.

Data sharing in the health sector represents a big problem due to privacy and security issues. Health data have tremendous value for organisations and criminals. The European Commission has classified health data as a unique resource owing to their ability to enable both retrospective and prospective research at a low cost. Similarly, the Organisation for Economic Co-operation and Development (OECD) encourages member nations to create and implement health data governance systems that protect individual privacy while allowing data sharing. This paper proposes adopting a blockchain framework to enable the transparent sharing of medical information among health entities in a secure environment. We develop a laboratory-based prototype using a design science research methodology (DSRM). This approach has its roots in the sciences of engineering and artificial intelligence, and its primary goal is to create relevant artefacts that add value to the fields in which they are used. We adopt a patient-centric approach, according to which a patient is the owner of their data and may allow hospitals and health professionals access to their data. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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13 pages, 2498 KiB

Open AccessArticle

Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function

by Usharani Bhimavarapu and Gopi Battineni

Healthcare 2023, 11(1), 97; https://doi.org/10.3390/healthcare11010097 - 28 Dec 2022

Cited by 8 | Viewed by 1545

Abstract

Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models, such as convolutional neural networks (CNNs), are largely [...] Read more.

Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models, such as convolutional neural networks (CNNs), are largely used in DR detection through the classification of blood vessel pixels from the remaining pixels. In this paper, an improved activation function was proposed for diagnosing DR from fundus images that automatically reduces loss and processing time. The DIARETDB0, DRIVE, CHASE, and Kaggle datasets were used to train and test the enhanced activation function in the different CNN models. The ResNet-152 model has the highest accuracy of 99.41% with the Kaggle dataset. This enhanced activation function is suitable for DR diagnosis from retinal fundus images. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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14 pages, 2494 KiB

Open AccessArticle

Survival Analysis of Oncological Patients Using Machine Learning Method

by Latefa Hamad Al Fryan and Malik Bader Alazzam

Healthcare 2023, 11(1), 80; https://doi.org/10.3390/healthcare11010080 - 27 Dec 2022

Cited by 1 | Viewed by 1654

Abstract

Currently, a considerable volume of information is collected and stored by large health institutions. These data come from medical records and hospital records, and the Hospital Cancer Registry is a database for integrating data from hospitals throughout Iraq. The data mining (DM) technique [...] Read more.

Currently, a considerable volume of information is collected and stored by large health institutions. These data come from medical records and hospital records, and the Hospital Cancer Registry is a database for integrating data from hospitals throughout Iraq. The data mining (DM) technique provides knowledge previously not visible in the database and can be used to predict trends or describe characteristics of the past. DM methods can include classification, generalisation, characterisation, clustering, association, evolution, pattern discovery, data visualisation, and rule-guided mining techniques to perform survival analyses that take into account all the patient’s medical record variables. For four of the eleven groups examined, this accuracy was relatively high. The database of patients treated by the Baghdad Teaching Hospital between 2018 and 2021 was examined using a classification of the most crucial variables for event prediction, and a distinctive pattern was found. Machine learning techniques allow a global assessment of the data that is available and produce results that can be interpreted as significant information for epidemiological studies, even in cases where the sample is small and there is a lack of information on several variables. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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32 pages, 3910 KiB

Open AccessArticle

An Analysis of Body Language of Patients Using Artificial Intelligence

by Rawad Abdulghafor, Abdelrahman Abdelmohsen, Sherzod Turaev, Mohammed A. H. Ali and Sharyar Wani

Healthcare 2022, 10(12), 2504; https://doi.org/10.3390/healthcare10122504 - 10 Dec 2022

Cited by 6 | Viewed by 4303

Abstract

In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in [...] Read more.

In recent decades, epidemic and pandemic illnesses have grown prevalent and are a regular source of concern throughout the world. The extent to which the globe has been affected by the COVID-19 epidemic is well documented. Smart technology is now widely used in medical applications, with the automated detection of status and feelings becoming a significant study area. As a result, a variety of studies have begun to focus on the automated detection of symptoms in individuals infected with a pandemic or epidemic disease by studying their body language. The recognition and interpretation of arm and leg motions, facial recognition, and body postures is still a developing field, and there is a dearth of comprehensive studies that might aid in illness diagnosis utilizing artificial intelligence techniques and technologies. This literature review is a meta review of past papers that utilized AI for body language classification through full-body tracking or facial expressions detection for various tasks such as fall detection and COVID-19 detection, it looks at different methods proposed by each paper, their significance and their results. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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14 pages, 2620 KiB

Open AccessArticle

Virtual Reality Combined with Artificial Intelligence (VR-AI) Reduces Hot Flashes and Improves Psychological Well-Being in Women with Breast and Ovarian Cancer: A Pilot Study

by Danny Horesh, Shaked Kohavi, Limor Shilony-Nalaboff, Naomi Rudich, Danielle Greenman, Joseph S. Feuerstein and Muhammad Rashid Abbasi

Healthcare 2022, 10(11), 2261; https://doi.org/10.3390/healthcare10112261 - 11 Nov 2022

Cited by 3 | Viewed by 3403

Abstract

Background and aims: Breast and ovarian cancers affect the lives of many women worldwide. Female cancer survivors often experience hot flashes, a subjective sensation of heat associated with objective signs of cutaneous vasodilatation and a subsequent drop in core temperature. Breast and Ovarian [...] Read more.

Background and aims: Breast and ovarian cancers affect the lives of many women worldwide. Female cancer survivors often experience hot flashes, a subjective sensation of heat associated with objective signs of cutaneous vasodilatation and a subsequent drop in core temperature. Breast and Ovarian cancer patients also suffer from sleep difficulties and mental health issues. The present study aimed to assess the effectiveness of Bubble, a novel artificial intelligence–virtual reality (AI–VR) intervention for the treatment of hot flashes in female breast or ovarian cancer patients. Methods: Forty-two women with breast and/or ovarian cancer participated in the study. The mean age was 47 years (range: 25–60 years). Patients suffered from hot flashes at different frequencies. They used Bubble, a virtual reality (VR) mobile psychological intervention based on elements from both cognitive behavioral therapy and mindfulness-based stress reduction. The intervention took place in a VR environment, in a winter wonderland setting called Frosty. Patients were instructed to use Bubble at home twice a day (morning and evening) and when experiencing a hot flash. Participants were asked to use the application for 24 consecutive days. Before and after this 24-day period, patients completed self-report questionnaires assessing hot flashes, general psychiatric distress, perceived stress, illness perception, sleep quality, and quality of life. Results: Between pre- and post-intervention, participants reported a significant reduction in the daily frequency of hot flashes, stress, general psychiatric distress, several domains of QOL, and sleep difficulties, as well as an improvement in illness perception. In addition, they reported very high satisfaction with Bubble. Importantly, both age and baseline levels of psychopathology moderated the effect of Bubble on sleep difficulties. Discussion: This study showed preliminary evidence for the potential of VR interventions in alleviating hot flashes and accompanying mental distress among those coping with breast and ovarian cancer. VR is a powerful therapeutic tool, able to address mind–body aspects in a direct, vivid way. More studies are needed in order to fully understand the potential of this unique intervention. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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26 pages, 3041 KiB

Open AccessArticle

Accelerating the Front End of Medicine: Three Digital Use Cases and HCI Implications

by Matthias Klumpp, André Hanelt, Maike Greve, Lutz M. Kolbe, Schahin Tofangchi, Florian Böhrnsen, Jens Jakob, Sylvia Kaczmarek, Ingo Börsting, Christopher Ehmke, Helena Düsing and Christian Juhra

Healthcare 2022, 10(11), 2176; https://doi.org/10.3390/healthcare10112176 - 30 Oct 2022

Viewed by 1943

Abstract

Digital applications in health care are a concurrent research and management question, where implementation experiences are a core field of information systems research. It also contributes to fighting pandemic crises like COVID-19 because contactless information flow and speed of diagnostics are improved. This [...] Read more.

Digital applications in health care are a concurrent research and management question, where implementation experiences are a core field of information systems research. It also contributes to fighting pandemic crises like COVID-19 because contactless information flow and speed of diagnostics are improved. This paper presents three digital application case studies from emergency medicine, administration management, and cancer diagnosis with AI support from the University Medical Centers of Münster and Göttingen in Germany. All cases highlight the potential of digitalization to increase speed and efficiency within the front end of medicine as the crucial phase before patient treatment starts. General challenges for health care project implementations and human-computer interaction (HCI) concepts in health care are derived and discussed, including the importance of specific processes together with user analysis and adaption. A derived concept for HCI includes the criteria speed, accuracy, modularity, and individuality to achieve sustainable improvements within the front end of medicine. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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12 pages, 2271 KiB

Open AccessArticle

Automated Bone Age Assessment: A New Three-Stage Assessment Method from Coarse to Fine

by Xinzheng Xu, Huihui Xu and Zhongnian Li

Healthcare 2022, 10(11), 2170; https://doi.org/10.3390/healthcare10112170 - 30 Oct 2022

Cited by 1 | Viewed by 3878

Abstract

Bone age assessment (BAA) based on X-ray imaging of the left hand and wrist can accurately reflect the degree of the body’s physiological development and physical condition. However, the traditional manual evaluation method relies too much on inefficient specialist labor. In this paper, [...] Read more.

Bone age assessment (BAA) based on X-ray imaging of the left hand and wrist can accurately reflect the degree of the body’s physiological development and physical condition. However, the traditional manual evaluation method relies too much on inefficient specialist labor. In this paper, to propose automatic BAA, we introduce a hierarchical convolutional neural network to detect the regions of interest (ROI) and classify the bone grade. Firstly, we establish a dataset of children’s BAA containing 2518 left hand X-rays. Then, we use the fine-grained classification to obtain the grade of the region of interest via object detection. Specifically, fine-grained classifiers are based on context-aware attention pooling (CAP). Finally, we perform the model assessment of bone age using the third version of the Tanner–Whitehouse (TW3) methodology. The end-to-end BAA system provides bone age values, the detection results of 13 ROIs, and the bone maturity of the ROIs, which are convenient for doctors to obtain information for operation. Experimental results on the public dataset and clinical dataset show that the performance of the proposed method is competitive. The accuracy of bone grading is 86.93%, and the mean absolute error (MAE) of bone age is 7.68 months on the clinical dataset. On public dataset, the MAE is 6.53 months. The proposed method achieves good performance in bone age assessment and is superior to existing fine-grained image classification methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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15 pages, 3776 KiB

Open AccessArticle

Clinical Application of Individualized 3D-Printed Templates in the Treatment of Condylar Osteochondroma

by Wen Ma, Shiwei Niu, Lidong Wang, Canbang Peng, Shuai Fu, Changbin Zhang, Qingying Cui, Sihang Wang, Ming Li and Yanhua Xu

Healthcare 2022, 10(11), 2163; https://doi.org/10.3390/healthcare10112163 - 29 Oct 2022

Cited by 1 | Viewed by 1390

Abstract

Background: Osteochondroma (OC) is one of the most common benign tumors of the long bones, but it rarely occurs in the maxillofacial skeleton. However, mandibular condylar OC often leads to severe facial deformity in affected patients, including facial asymmetry, deviation of the chin, [...] Read more.

Background: Osteochondroma (OC) is one of the most common benign tumors of the long bones, but it rarely occurs in the maxillofacial skeleton. However, mandibular condylar OC often leads to severe facial deformity in affected patients, including facial asymmetry, deviation of the chin, and malocclusion. This study aimed to explore the clinical application of individualized 3D-printed templates to accurately and effectively treat condylar OC. Methods: A total of 8 patients with mandibular condylar OC were treated from July 2015 to August 2021. The enrolled patients (5 women and 3 men) had a median age of 27 years (range: 21–32 years). All patients exhibited symptoms of facial asymmetry and occlusal disorders preoperatively. The digital software used to virtually design the process consisted of three-dimensional reconstruction, 3D-cephalometry analysis, virtual surgery, individualized templates, and postoperative facial soft-tissue prediction. A set of 3D-printed templates (DOS and DOT) were used in all cases to stabilize the occlusion and guide the osteotomy. Then, pre- and post-operative complications, mouth opening, clinical signs, and the accuracy of the CT imaging analysis were all evaluated. All the measurement data were presented as means ± SD; Bonferroni and Tamhane T2 multiple comparison tests were used to examine the differences between the groups. Results: All patients healed uneventfully. None of the patients exhibited facial nerve injury at follow-up. In comparing the condylar segments with T0p and T1, the average deviation of the condylar segments was 0.5796 mm, indicating that the post-operative reconstructed condyles showed a high degree of similarity to the reconstruction results of the virtual surgical plan. Conclusions: Individualized 3D-printed templates simplified surgical procedures and improved surgical accuracy, proving to be an effective method for the treatment of patients with slight asymmetric deformities secondary to condylar OC. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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17 pages, 6611 KiB

Open AccessArticle

Examining Cognitive Factors for Alzheimer’s Disease Progression Using Computational Intelligence

by Fadi Thabtah, Swan Ong and David Peebles

Healthcare 2022, 10(10), 2045; https://doi.org/10.3390/healthcare10102045 - 17 Oct 2022

Cited by 5 | Viewed by 1723

Abstract

Prognosis of Alzheimer’s disease (AD) progression has been recognized as a challenging problem due to the massive numbers of cognitive, and pathological features recorded for patients and controls. While there have been many studies investigated the diagnosis of dementia using pathological characteristics, predicting [...] Read more.

Prognosis of Alzheimer’s disease (AD) progression has been recognized as a challenging problem due to the massive numbers of cognitive, and pathological features recorded for patients and controls. While there have been many studies investigated the diagnosis of dementia using pathological characteristics, predicting the advancement of the disease using cognitive elements has not been heavily studied particularly using technologies like artificial intelligence and machine learning. This research aims at evaluating items of the Alzheimer’s Disease Assessment Scale-Cognitive 13 (ADAS-Cog-13) test to determine key cognitive items that influence the progression of AD. A methodology that consists of machine learning and feature selection (FS) techniques was designed, implemented, and then tested against real data observations (cases and controls) of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository with a narrow scope on cognitive items of the ADAS-Cog-13 test. Results obtained by ten-fold cross validation and using dissimilar classification and FS techniques revealed that the decision tree algorithm produced classification models with the best performing results from the cognitive items. For ADAS-Cog-13 test, memory and learning features including word recall, delayed word recall and word recognition were the key items pinpointing to AD advancement. When these three cognitive items are processed excluding demographics by C4.5 algorithm the models derived showed 82.90% accuracy, 87.60% sensitivity and 78.20% specificity. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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14 pages, 2752 KiB

Open AccessArticle

Age-Stratified Analysis of COVID-19 Outcome Using Machine Learning Predictive Models

by Juan L. Domínguez-Olmedo, Álvaro Gragera-Martínez, Jacinto Mata and Victoria Pachón

Healthcare 2022, 10(10), 2027; https://doi.org/10.3390/healthcare10102027 - 14 Oct 2022

Viewed by 1129

Abstract

Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a [...] Read more.

Since the emergence of COVID-19, most health systems around the world have experienced a series of spikes in the number of infected patients, leading to collapse of the health systems in many countries. The use of clinical laboratory tests can serve as a discriminatory method for disease severity, defining the profile of patients with a higher risk of mortality. In this paper, we study the results of applying predictive models to data regarding COVID-19 outcome, using three datasets after age stratification of patients. The extreme gradient boosting (XGBoost) algorithm was employed as the predictive method, yielding excellent results. The area under the receiving operator characteristic curve (AUROC) value was 0.97 for the subgroup of patients up to 65 years of age. In addition, SHAP (Shapley additive explanations) was used to analyze the feature importance in the resulting models. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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18 pages, 2370 KiB

Open AccessArticle

Intelligent Monitoring Model for Fall Risks of Hospitalized Elderly Patients

by Amal H. Alharbi and Hanan A. Hosni Mahmoud

Healthcare 2022, 10(10), 1896; https://doi.org/10.3390/healthcare10101896 - 28 Sep 2022

Cited by 2 | Viewed by 1499

Abstract

Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if [...] Read more.

Early detection of high fall risk is an important process of fall prevention in hospitalized elderly patients. Hospitalized elderly patients can face several falling risks. Monitoring systems can be utilized to protect health and lives, and monitoring models can be less effective if the alarm is not invoked in real time. Therefore, in this paper we propose a monitoring prediction system that incorporates artificial intelligence. The proposed system utilizes a scalable clustering technique, namely the Catboost method, for binary classification. These techniques are executed on the Snowflake platform to rapidly predict safe and risky incidence for hospitalized elderly patients. A later stage employs a deep learning model (DNN) that is based on a convolutional neural network (CNN). Risky incidences are further classified into various monitoring alert types (falls, falls with broken bones, falls that lead to death). At this phase, the model employs adaptive sampling techniques to elucidate the unbalanced overfitting in the datasets. A performance study utilizes the benchmarks datasets, namely SERV-112 and SV-S2017 of the image sequences for assessing accuracy. The simulation depicts that the system has higher true positive counts in case of all health-related risk incidences. The proposed system depicts real-time classification speed with lower training time. The performance of the proposed multi-risk prediction is high at 87.4% in the SERV-112 dataset and 98.71% in the SV-S2017 dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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20 pages, 1560 KiB

Open AccessArticle

Classification Algorithms Used in Predicting Glaucoma Progression

by Filip Tarcoveanu, Florin Leon, Silvia Curteanu, Dorin Chiselita, Camelia Margareta Bogdanici and Nicoleta Anton

Healthcare 2022, 10(10), 1831; https://doi.org/10.3390/healthcare10101831 - 22 Sep 2022

Cited by 6 | Viewed by 2060

Abstract

In this paper, various machine learning algorithms were used in order to predict the evolution of open-angle glaucoma (POAG). The datasets were built containing clinical observations and objective measurements made at the Countess of Chester Hospital in the UK and at the “St. [...] Read more.

In this paper, various machine learning algorithms were used in order to predict the evolution of open-angle glaucoma (POAG). The datasets were built containing clinical observations and objective measurements made at the Countess of Chester Hospital in the UK and at the “St. Spiridon” Hospital of Iași, Romania. Using these datasets, different classification problems were proposed. The evaluation of glaucoma progression was conducted based on parameters such as VFI (Visual field index), MD (Mean Deviation), PSD (Pattern standard deviation), and RNFL (Retinal Nerve Fiber Layer). As classification tools, the following algorithms were used: Multilayer Perceptron, Random Forest, Random Tree, C4.5, k-Nearest Neighbors, Support Vector Machine, and Non-Nested Generalized Exemplars. The best results, with an accuracy of over 90%, were obtained with Multilayer Perceptron and Random Forest algorithms. The NNGE algorithm also proved very useful in creating a hierarchy of the input values according to their influence (weight) on the considered outputs. On the other hand, the decision tree algorithms gave us insight into the logic used in their classification, which is of practical importance in obtaining additional information regarding the rationale behind a certain rule or decision. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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15 pages, 2142 KiB

Open AccessArticle

An Immersive Human-Robot Interactive Game Framework Based on Deep Learning for Children’s Concentration Training

by Li Liu, Yangguang Liu, Xiao-Zhi Gao and Xiaomin Zhang

Healthcare 2022, 10(9), 1779; https://doi.org/10.3390/healthcare10091779 - 15 Sep 2022

Cited by 1 | Viewed by 1749

Abstract

In order to alleviate bottlenecks such as the lack of professional teachers, inattention during training processes, and low effectiveness in concentration training, we have proposed an immersive human–robot interactive (HRI) game framework based on deep learning for children’s concentration training and demonstrated its [...] Read more.

In order to alleviate bottlenecks such as the lack of professional teachers, inattention during training processes, and low effectiveness in concentration training, we have proposed an immersive human–robot interactive (HRI) game framework based on deep learning for children’s concentration training and demonstrated its use through human–robot interactive games based on gesture recognition. The HRI game framework includes four functional modules: video data acquisition, image recognition modeling, a deep learning algorithm (YOLOv5), and information feedback. First, we built a gesture recognition model containing 10,000 pictures of children’s gestures, using the YOLOv5 algorithm. The average accuracy in recognition trainingwas 98.7%. Second, we recruited 120 children with attention deficits (aged from 9 to 12 years) to play the HRI games, including 60 girls and 60 boys. In the HRI game experiment, we obtained 8640 sample data, which were normalized and processed.According to the results, we found that the girls had better visual short-term memory and a shorter response time than boys. The research results showed that HRI games had a high efficacy, convenience, and full freedom, making them appropriate for children’s concentration training. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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18 pages, 4519 KiB

Open AccessArticle

Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA

by Joaquim Carreras

Healthcare 2022, 10(8), 1550; https://doi.org/10.3390/healthcare10081550 - 16 Aug 2022

Cited by 6 | Viewed by 3609

Abstract

Celiac disease is a common immune-related inflammatory disease of the small intestine caused by gluten in genetically predisposed individuals. This research is a proof-of-concept exercise focused on using Artificial Intelligence (AI) and an autoimmune discovery gene panel to predict and model celiac disease. [...] Read more.

Celiac disease is a common immune-related inflammatory disease of the small intestine caused by gluten in genetically predisposed individuals. This research is a proof-of-concept exercise focused on using Artificial Intelligence (AI) and an autoimmune discovery gene panel to predict and model celiac disease. Conventional bioinformatics, gene set enrichment analysis (GSEA), and several machine learning and neural network techniques were used on a publicly available dataset (GSE164883). Machine learning and deep learning included C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network (multilayer perceptron). As a result, the gene panel predicted celiac disease with high accuracy (95–100%). Several pathogenic genes were identified, some of the immune checkpoint and immuno-oncology pathways. They included CASP3, CD86, CTLA4, FASLG, GZMB, IFNG, IL15RA, ITGAX, LAG3, MMP3, MUC1, MYD88, PRDM1, RGS1, etc. Among them, B and T lymphocyte associated (BTLA, CD272) was highlighted and validated at the protein level by immunohistochemistry in an independent series of cases. Celiac disease was characterized by high BTLA, expressed by inflammatory cells of the lamina propria. In conclusion, artificial intelligence predicted celiac disease using an autoimmune discovery gene panel. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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14 pages, 2700 KiB

Open AccessArticle

Artificial Intelligence Analysis of Ulcerative Colitis Using an Autoimmune Discovery Transcriptomic Panel

by Joaquim Carreras

Healthcare 2022, 10(8), 1476; https://doi.org/10.3390/healthcare10081476 - 05 Aug 2022

Cited by 6 | Viewed by 2626

Abstract

Ulcerative colitis is a bowel disease of unknown cause. This research is a proof-of-concept exercise focused on determining whether it is possible to identify the genes associated with ulcerative colitis using artificial intelligence. Several machine learning and artificial neural networks analyze using an [...] Read more.

Ulcerative colitis is a bowel disease of unknown cause. This research is a proof-of-concept exercise focused on determining whether it is possible to identify the genes associated with ulcerative colitis using artificial intelligence. Several machine learning and artificial neural networks analyze using an autoimmune discovery transcriptomic panel of 755 genes to predict and model ulcerative colitis versus healthy donors. The dataset GSE38713 of 43 cases from the Hospital Clinic of Barcelona was selected, and 16 models were used, including C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network. Conventional analysis, including volcano plot and gene set enrichment analysis (GSEA), were also performed. As a result, ulcerative colitis was successfully predicted with several machine learning techniques and artificial neural networks (multilayer perceptron), with an overall accuracy of 95–100%, and relevant pathogenic genes were highlighted. One of them, programmed cell death 1 ligand 1 (PD-L1, CD274, PDCD1LG1, B7-H1) was validated in a series from the Tokai University Hospital by immunohistochemistry. In conclusion, artificial intelligence analysis of transcriptomic data of ulcerative colitis is a feasible analytical strategy. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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16 pages, 744 KiB

Open AccessArticle

The Prediction of Peritoneal Carcinomatosis in Patients with Colorectal Cancer Using Machine Learning

by Valentin Bejan, Elena-Niculina Dragoi, Silvia Curteanu, Viorel Scripcariu and Bogdan Filip

Healthcare 2022, 10(8), 1425; https://doi.org/10.3390/healthcare10081425 - 29 Jul 2022

Cited by 2 | Viewed by 1459

Abstract

The incidence of colon, rectal, and colorectal cancer is very high, and diagnosis is often made in the advanced stages of the disease. In cases where peritoneal carcinomatosis is limited, patients can benefit from newer treatment options if the disease is promptly identified, [...] Read more.

The incidence of colon, rectal, and colorectal cancer is very high, and diagnosis is often made in the advanced stages of the disease. In cases where peritoneal carcinomatosis is limited, patients can benefit from newer treatment options if the disease is promptly identified, and they are referred to specialized centers. Therefore, an essential diagnostic benefit would be identifying those factors that could lead to early diagnosis. A retrospective study was performed using patient data gathered from 2010 to 2020. The collected data were represented by routine blood tests subjected to stringent inclusion and exclusion criteria. In order to determine the presence or absence of peritoneal carcinomatosis in colorectal cancer patients, three types of machine learning approaches were applied: a neuro-evolutive methodology based on artificial neural network (ANN), support vector machines (SVM), and random forests (RF), all combined with differential evolution (DE). The optimizer (DE in our case) determined the internal and structural parameters that defined the ANN, SVM, and RF in their optimal form. The RF strategy obtained the best accuracy in the testing phase (0.75). Using this RF model, a sensitivity analysis was applied to determine the influence of each parameter on the presence or absence of peritoneal carcinomatosis. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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18 pages, 511 KiB

Open AccessArticle

Hard Voting Ensemble Approach for the Detection of Type 2 Diabetes in Mexican Population with Non-Glucose Related Features

by Jorge A. Morgan-Benita, Carlos E. Galván-Tejada, Miguel Cruz, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, Jose G. Arceo-Olague, Huizilopoztli Luna-García and José M. Celaya-Padilla

Healthcare 2022, 10(8), 1362; https://doi.org/10.3390/healthcare10081362 - 22 Jul 2022

Cited by 3 | Viewed by 2098

Abstract

Type 2 diabetes mellitus (T2DM) represents one of the biggest health problems in Mexico, and it is extremely important to early detect this disease and its complications. For a noninvasive detection of T2DM, a machine learning (ML) approach that uses ensemble classification models [...] Read more.

Type 2 diabetes mellitus (T2DM) represents one of the biggest health problems in Mexico, and it is extremely important to early detect this disease and its complications. For a noninvasive detection of T2DM, a machine learning (ML) approach that uses ensemble classification models with dichotomous output that is also fast and effective for early detection and prediction of T2D can be used. In this article, an ensemble technique by hard voting is designed and implemented using generalized linear regression (GLM), support vector machines (SVM) and artificial neural networks (ANN) for the classification of T2DM patients. In the materials and methods as a first step, the data is balanced, standardized, imputed and integrated into the three models to classify the patients in a dichotomous result. For the selection of features, an implementation of LASSO is developed, with a 10-fold cross-validation and for the final validation, the Area Under the Curve (AUC) is used. The results in LASSO showed 12 features, which are used in the implemented models to obtain the best possible scenario in the developed ensemble model. The algorithm with the best performance of the three is SVM, this model obtained an AUC of 92% ± 3%. The ensemble model built with GLM, SVM and ANN obtained an AUC of 90% ± 3%. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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18 pages, 2294 KiB

Open AccessArticle

New Method to Implement and Analysis of Medical System in Real Time

by Yahia Zakria Abd Elgawad, Mohamed I. Youssef, Tarek Mahmoud Nasser, Amir Almslmany, Ahmed S. I. Amar, Abdelrhman Adel Mohamed, Naser Ojaroudi Parchin, Raed A. Abd-Alhameed, Heba G. Mohamed and Karim H. Moussa

Healthcare 2022, 10(7), 1357; https://doi.org/10.3390/healthcare10071357 - 21 Jul 2022

Cited by 2 | Viewed by 1493

Abstract

The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this [...] Read more.

The use of information technology and technological medical devices has contributed significantly to the transformation of healthcare. Despite that, many problems have arisen in diagnosing or predicting diseases, either as a result of human errors or lack of accuracy of measurements. Therefore, this paper aims to provide an integrated health monitoring system to measure vital parameters and diagnose or predict disease. Through this work, the percentage of various gases in the blood through breathing is determined, vital parameters are measured and their effect on feelings is analyzed. A supervised learning model is configured to predict and diagnose based on biometric measurements. All results were compared with the results of the Omron device as a reference device. The results proved that the proposed design overcame many problems as it contributed to expanding the database of vital parameters and providing analysis on the effect of emotions on vital indicators. The accuracy of the measurements also reached 98.8% and the accuracy of diagnosing COVID-19 was 64%. The work also presents a user interface model for clinicians as well as for smartphones using the Internet of things. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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15 pages, 5492 KiB

Open AccessArticle

Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal

by Julieta G. Rodríguez-Ruiz, Carlos E. Galván-Tejada, Huizilopoztli Luna-García, Hamurabi Gamboa-Rosales, José M. Celaya-Padilla, José G. Arceo-Olague and Jorge I. Galván Tejada

Healthcare 2022, 10(7), 1256; https://doi.org/10.3390/healthcare10071256 - 05 Jul 2022

Cited by 4 | Viewed by 2193

Abstract

Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s underdiagnosis. Still, [...] Read more.

Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s underdiagnosis. Still, most current global metrics hover between 0.33% and 0.75%. Machine-learning scientists use data from diverse sources to analyze, classify, or predict to improve the psychiatric attention, diagnosis, and treatment of MDD, schizophrenia, and other psychiatric conditions. Motor activity data are gaining popularity in mental illness diagnosis assistance because they are a cost-effective and noninvasive method. In the knowledge discovery in databases (KDD) framework, a model to classify depressive and schizophrenic patients from healthy controls is constructed using accelerometer data. Taking advantage of the multiple sleep disorders caused by mental disorders, the main objective is to increase the model’s accuracy by employing only data from night-time activity. To compare the classification between the stages of the day and improve the accuracy of the classification, the total activity signal was cut into hourly time lapses and then grouped into subdatasets depending on the phases of the day: morning (06:00–11:59), afternoon (12:00–17:59), evening (18:00–23:59), and night (00:00–05:59). Random forest classifier (RFC) is the algorithm proposed for multiclass classification, and it uses accuracy, recall, precision, the Matthews correlation coefficient, and F1 score to measure its efficiency. The best model was night-featured data and RFC, with 98% accuracy for the classification of three classes. The effectiveness of this experiment leads to less monitoring time for patients, reducing stress and anxiety, producing more efficient models, using wearables, and increasing the amount of data. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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14 pages, 4180 KiB

Open AccessArticle

Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone

by Juyoung Hong, Jiwon Kim, Sunmi Kim, Jaewon Oh, Deokjong Lee, San Lee, Jinsun Uh, Juhong Yoon and Yukyung Choi

Healthcare 2022, 10(7), 1189; https://doi.org/10.3390/healthcare10071189 - 24 Jun 2022

Cited by 8 | Viewed by 2586

Abstract

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used [...] Read more.

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a “Mental Health Protector” application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system’s prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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14 pages, 1953 KiB

Open AccessArticle

Forecasting Hospital Readmissions with Machine Learning

by Panagiotis Michailidis, Athanasia Dimitriadou, Theophilos Papadimitriou and Periklis Gogas

Healthcare 2022, 10(6), 981; https://doi.org/10.3390/healthcare10060981 - 25 May 2022

Cited by 4 | Viewed by 3090

Abstract

Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for [...] Read more.

Hospital readmissions are regarded as a compounding economic factor for healthcare systems. In fact, the readmission rate is used in many countries as an indicator of the quality of services provided by a health institution. The ability to forecast patients’ readmissions allows for timely intervention and better post-discharge strategies, preventing future life-threatening events, and reducing medical costs to either the patient or the healthcare system. In this paper, four machine learning models are used to forecast readmissions: support vector machines with a linear kernel, support vector machines with an RBF kernel, balanced random forests, and weighted random forests. The dataset consists of 11,172 actual records of hospitalizations obtained from the General Hospital of Komotini “Sismanogleio” with a total of 24 independent variables. Each record is composed of administrative, medical-clinical, and operational variables. The experimental results indicate that the balanced random forest model outperforms the competition, reaching a sensitivity of 0.70 and an AUC value of 0.78. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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22 pages, 3518 KiB

Open AccessArticle

Learning from Acceleration Data to Differentiate the Posture, Dynamic and Static Work of the Back: An Experimental Setup

by Elena Camelia Muşat and Stelian Alexandru Borz

Healthcare 2022, 10(5), 916; https://doi.org/10.3390/healthcare10050916 - 15 May 2022

Cited by 3 | Viewed by 1919

Abstract

Information on body posture, postural change, and dynamic and static work is essential in understanding biomechanical exposure and has many applications in ergonomics and healthcare. This study aimed at evaluating the possibility of using triaxial acceleration data to classify postures and to differentiate [...] Read more.

Information on body posture, postural change, and dynamic and static work is essential in understanding biomechanical exposure and has many applications in ergonomics and healthcare. This study aimed at evaluating the possibility of using triaxial acceleration data to classify postures and to differentiate between dynamic and static work of the back in an experimental setup, based on a machine learning (ML) approach. A movement protocol was designed to cover the essential degrees of freedom of the back, and a subject wearing a triaxial accelerometer implemented this protocol. Impulses and oscillations from the signals were removed by median filtering, then the filtered dataset was fed into two ML algorithms, namely a multilayer perceptron with back propagation (MLPBNN) and a random forest (RF), with the aim of inferring the most suitable algorithm and architecture for detecting dynamic and static work, as well as for correctly classifying the postures of the back. Then, training and testing subsets were delimitated and used to evaluate the learning and generalization ability of the ML algorithms for the same classification problems. The results indicate that ML has a lot of potential in differentiating between dynamic and static work, depending on the type of algorithm and its architecture, and the data quantity and quality. In particular, MLPBNN can be used to better differentiate between dynamic and static work when tuned properly. In addition, static work and the associated postures were better learned and generalized by the MLPBNN, a fact that could provide the basis for cheap real-world offline applications with the aim of getting time-scaled postural profiling data by accounting for the static postures. Although it wasn’t the case in this study, on bigger datasets, the use of MLPBPNN may come at the expense of high computational costs in the training phase. The study also discusses the factors that may improve the classification performance in the testing phase and sets new directions of research. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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10 pages, 541 KiB

Open AccessArticle

Predicting the Risk of Future Multiple Suicide Attempt among First-Time Suicide Attempters: Implications for Suicide Prevention Policy

by I-Li Lin, Jean Yu-Chen Tseng, Hui-Ting Tung, Ya-Han Hu and Zi-Hung You

Healthcare 2022, 10(4), 667; https://doi.org/10.3390/healthcare10040667 - 02 Apr 2022

Cited by 1 | Viewed by 2044

Abstract

Suicide is listed in the top ten causes of death in Taiwan. Previous studies have pointed out that psychiatric patients having suicide attempts in their history are more likely to attempt suicide again than non-psychiatric patients. Therefore, how to predict the future multiple [...] Read more.

Suicide is listed in the top ten causes of death in Taiwan. Previous studies have pointed out that psychiatric patients having suicide attempts in their history are more likely to attempt suicide again than non-psychiatric patients. Therefore, how to predict the future multiple suicide attempts of psychiatric patients is an important issue of public health. Different from previous studies, we collect the psychiatric patients who have a suicide diagnosis in the National Health Insurance Research Database (NHIRD) as the study cohort. Study variables include psychiatric patients’ characteristics, medical behavior characteristics, physician characteristics, and hospital characteristics. Three machine learning techniques, including decision tree (DT), support vector machine (SVM), and artificial neural network (ANN), are used to develop models for predicting the risk of future multiple suicide attempts. The Adaboost technique is further used to improve prediction performance in model development. The experimental results show that Adaboost+DT performs the best in predicting the behavior of multiple suicide attempts among psychiatric patients. The findings of this study can help clinical staffs to early identify high-risk patients and improve the effectiveness of suicide prevention. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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18 pages, 1820 KiB

Open AccessArticle

CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals

by Emrah Aydemir, Sengul Dogan, Mehmet Baygin, Chui Ping Ooi, Prabal Datta Barua, Turker Tuncer and U. Rajendra Acharya

Healthcare 2022, 10(4), 643; https://doi.org/10.3390/healthcare10040643 - 29 Mar 2022

Cited by 17 | Viewed by 2261

Abstract

Background and Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated [...] Read more.

Background and Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator. Therefore, the presented feature extractor was named as the cyclic group of prime order pattern, CGP17Pat. Using the proposed CGP17Pat, a new multilevel feature extraction model is presented. To choose a highly distinctive feature, iterative neighborhood component analysis (INCA) was used, and these features were classified using k-nearest neighbors (kNN) with the 10-fold cross-validation and leave-one-subject-out (LOSO) validation techniques. Finally, iterative hard majority voting was employed in the last phase to obtain channel-wise results, and the general results were calculated. Results: The presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy. Conclusions: The findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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17 pages, 8567 KiB

Open AccessArticle

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

by Esra Sivari, Mehmet Serdar Güzel, Erkan Bostanci and Alok Mishra

Healthcare 2022, 10(3), 580; https://doi.org/10.3390/healthcare10030580 - 20 Mar 2022

Cited by 11 | Viewed by 2582

Abstract

It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous [...] Read more.

It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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11 pages, 1488 KiB

Open AccessReview

Artificial Intelligence in NAFLD: Will Liver Biopsy Still Be Necessary in the Future?

by Lei Zhang and Yilei Mao

Healthcare 2023, 11(1), 117; https://doi.org/10.3390/healthcare11010117 - 30 Dec 2022

Cited by 2 | Viewed by 3491

Abstract

As the advanced form of nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH) will significantly increase the risks of liver fibrosis, cirrhosis, and HCC. However, there is no non-invasive method to distinguish NASH from NAFLD so far. Additionally, liver biopsy remains the gold [...] Read more.

As the advanced form of nonalcoholic fatty liver disease (NAFLD), nonalcoholic steatohepatitis (NASH) will significantly increase the risks of liver fibrosis, cirrhosis, and HCC. However, there is no non-invasive method to distinguish NASH from NAFLD so far. Additionally, liver biopsy remains the gold standard to diagnose NASH, which is not appropriate for routine screening. Recently, artificial intelligence (AI) is under rapid development in many aspects of medicine. Additionally, the application of AI in clinical information may have the potential to diagnose NASH non-invasively. This review summarizes the latest research using AI, specifically machine learning, to facilitate the diagnosis, prognosis, and monitoring of NAFLD. Additionally, according to our prior results, this work proposes future development in this area. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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19 pages, 6100 KiB

Open AccessReview

Breast Cancer Dataset, Classification and Detection Using Deep Learning

by Muhammad Shahid Iqbal, Waqas Ahmad, Roohallah Alizadehsani, Sadiq Hussain and Rizwan Rehman

Healthcare 2022, 10(12), 2395; https://doi.org/10.3390/healthcare10122395 - 29 Nov 2022

Cited by 9 | Viewed by 8489

Abstract

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients’ treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are [...] Read more.

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients’ treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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14 pages, 1303 KiB

Open AccessReview

A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques

by Fatma Alshohoumi, Abdullah Al-Hamdani, Rachid Hedjam, AbdulRahman AlAbdulsalam and Adhari Al Zaabi

Healthcare 2022, 10(10), 2075; https://doi.org/10.3390/healthcare10102075 - 19 Oct 2022

Viewed by 1847

Abstract

An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and [...] Read more.

An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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17 pages, 2376 KiB

Open AccessReview

Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce

by Shahid Ud Din Wani, Nisar Ahmad Khan, Gaurav Thakur, Surya Prakash Gautam, Mohammad Ali, Prawez Alam, Sultan Alshehri, Mohammed M. Ghoneim and Faiyaz Shakeel

Healthcare 2022, 10(4), 608; https://doi.org/10.3390/healthcare10040608 - 24 Mar 2022

Cited by 33 | Viewed by 9474

Abstract

Artificial intelligence (AI) has been described as one of the extremely effective and promising scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets [...] Read more.

Artificial intelligence (AI) has been described as one of the extremely effective and promising scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets of healthcare, as well as regulatory procedures within providers, payers, and pharmaceutical companies, may be transformed by these innovations. As a result, the purpose of this review is to identify the potential machine learning applications in the field of infectious diseases and the general healthcare system. The literature on this topic was extracted from various databases, such as Google, Google Scholar, Pubmed, Scopus, and Web of Science. The articles having important information were selected for this review. The most challenging task for AI in such healthcare sectors is to sustain its adoption in daily clinical practice, regardless of whether the programs are scalable enough to be useful. Based on the summarized data, it has been concluded that AI can assist healthcare staff in expanding their knowledge, allowing them to spend more time providing direct patient care and reducing weariness. Overall, we might conclude that the future of “conventional medicine” is closer than we realize, with patients seeing a computer first and subsequently a doctor. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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17 pages, 488 KiB

Open AccessSystematic Review

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

by Riccardo Rescinito, Matteo Ratti, Anil Babu Payedimarri and Massimiliano Panella

Healthcare 2023, 11(11), 1617; https://doi.org/10.3390/healthcare11111617 - 01 Jun 2023

Cited by 4 | Viewed by 1942

Abstract

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) [...] Read more.

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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15 pages, 2563 KiB

Open AccessTechnical Note

A Curriculum Batching Strategy for Automatic ICD Coding with Deep Multi-Label Classification Models

by Yaqiang Wang, Xu Han, Xuechao Hao, Tao Zhu and Hongping Shu

Healthcare 2022, 10(12), 2397; https://doi.org/10.3390/healthcare10122397 - 29 Nov 2022

Viewed by 925

Abstract

The International Classification of Diseases (ICD) has an important role in building applications for clinical medicine. Extremely large ICD coding label sets and imbalanced label distribution bring the problem of inconsistency between the local batch data distribution and the global training data distribution [...] Read more.

The International Classification of Diseases (ICD) has an important role in building applications for clinical medicine. Extremely large ICD coding label sets and imbalanced label distribution bring the problem of inconsistency between the local batch data distribution and the global training data distribution into the minibatch gradient descent (MBGD)-based training procedure for deep multi-label classification models for automatic ICD coding. The problem further leads to an overfitting issue. In order to improve the performance and generalization ability of the deep learning automatic ICD coding model, we proposed a simple and effective curriculum batching strategy in this paper for improving the MBGD-based training procedure. This strategy generates three batch sets offline through applying three predefined sampling algorithms. These batch sets satisfy a uniform data distribution, a shuffling data distribution and the original training data distribution, respectively, and the learning tasks corresponding to these batch sets range from simple to complex. Experiments show that, after replacing the original shuffling algorithm-based batching strategy with the proposed curriculum batching strategy, the performance of the three investigated deep multi-label classification models for automatic ICD coding all have dramatic improvements. At the same time, the models avoid the overfitting issue and all show better ability to learn the long-tailed label information. The performance is also better than a SOTA label set reconstruction model. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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20 pages, 735 KiB

Open AccessSystematic Review

Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes—A Systematic Review

by Stepan Feduniw, Dawid Golik, Anna Kajdy, Michał Pruc, Jan Modzelewski, Dorota Sys, Sebastian Kwiatkowski, Elżbieta Makomaska-Szaroszyk and Michał Rabijewski

Healthcare 2022, 10(11), 2164; https://doi.org/10.3390/healthcare10112164 - 29 Oct 2022

Cited by 6 | Viewed by 2199

Abstract

(1) Background: AI-based solutions could become crucial for the prediction of pregnancy disorders and complications. This study investigated the evidence for applying artificial intelligence methods in obstetric pregnancy risk assessment and adverse pregnancy outcome prediction. (2) Methods: Authors screened the following databases: Pubmed/MEDLINE, [...] Read more.

(1) Background: AI-based solutions could become crucial for the prediction of pregnancy disorders and complications. This study investigated the evidence for applying artificial intelligence methods in obstetric pregnancy risk assessment and adverse pregnancy outcome prediction. (2) Methods: Authors screened the following databases: Pubmed/MEDLINE, Web of Science, Cochrane Library, EMBASE, and Google Scholar. This study included all the evaluative studies comparing artificial intelligence methods in predicting adverse pregnancy outcomes. The PROSPERO ID number is CRD42020178944, and the study protocol was published before this publication. (3) Results: AI application was found in nine groups: general pregnancy risk assessment, prenatal diagnosis, pregnancy hypertension disorders, fetal growth, stillbirth, gestational diabetes, preterm deliveries, delivery route, and others. According to this systematic review, the best artificial intelligence application for assessing medical conditions is ANN methods. The average accuracy of ANN methods was established to be around 80–90%. (4) Conclusions: The application of AI methods as a digital software can help medical practitioners in their everyday practice during pregnancy risk assessment. Based on published studies, models that used ANN methods could be applied in APO prediction. Nevertheless, further studies could identify new methods with an even better prediction potential. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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9 pages, 812 KiB

Open AccessProtocol

Deep Learning Chest CT for Clinically Precise Prediction of Sepsis-Induced Acute Respiratory Distress Syndrome: A Protocol for an Observational Ambispective Cohort Study

by Han Li, Yang Gu, Xun Liu, Xiaoling Yi, Ziying Li, Yunfang Yu, Tao Yu and Li Li

Healthcare 2022, 10(11), 2150; https://doi.org/10.3390/healthcare10112150 - 28 Oct 2022

Cited by 1 | Viewed by 1444

Abstract

Background: Sepsis commonly causes acute respiratory distress syndrome (ARDS), and ARDS contributes to poor prognosis in sepsis patients. Early prediction of ARDS for sepsis patients remains a clinical challenge. This study aims to develop and validate chest computed tomography (CT) radiomic-based signatures for [...] Read more.

Background: Sepsis commonly causes acute respiratory distress syndrome (ARDS), and ARDS contributes to poor prognosis in sepsis patients. Early prediction of ARDS for sepsis patients remains a clinical challenge. This study aims to develop and validate chest computed tomography (CT) radiomic-based signatures for early prediction of ARDS and assessment of individual severity in sepsis patients. Methods: In this ambispective observational cohort study, a deep learning model, a sepsis-induced acute respiratory distress syndrome (SI-ARDS) prediction neural network, will be developed to extract radiomics features of chest CT from sepsis patients. The datasets will be collected from these retrospective and prospective cohorts, including 400 patients diagnosed with sepsis-3 definition during a period from 1 May 2015 to 30 May 2022. 160 patients of the retrospective cohort will be selected as a discovering group to reconstruct the model and 40 patients of the retrospective cohort will be selected as a testing group for internal validation. Additionally, 200 patients of the prospective cohort from two hospitals will be selected as a validating group for external validation. Data pertaining to chest CT, clinical information, immune-associated inflammatory indicators and follow-up will be collected. The primary outcome is to develop and validate the model, predicting in-hospital incidence of SI-ARDS. Finally, model performance will be evaluated using the area under the curve (AUC) of receiver operating characteristic (ROC), sensitivity and specificity, using internal and external validations. Discussion: Present studies reveal that early identification and classification of the SI-ARDS is essential to improve prognosis and disease management. Chest CT has been sought as a useful diagnostic tool to identify ARDS. However, when characteristic imaging findings were clearly presented, delays in diagnosis and treatment were impossible to avoid. In this ambispective cohort study, we hope to develop a novel model incorporating radiomic signatures and clinical signatures to provide an easy-to-use and individualized prediction of SI-ARDS occurrence and severe degree in patients at early stage. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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5 pages, 204 KiB

Open AccessCommentary

Incorporation of MyDispense, a Virtual Pharmacy Simulation, into Extemporaneous Formulation Laboratories

by Joseph A. Nicolazzo, Sara Chuang and Vivienne Mak

Healthcare 2022, 10(8), 1489; https://doi.org/10.3390/healthcare10081489 - 08 Aug 2022

Cited by 6 | Viewed by 1406

Abstract

A core competency of Australian Pharmacy graduates is to prepare and compound extemporaneous formulations. Students in our pharmacy course would traditionally formulate extemporaneous products in laboratory classes while simultaneously preparing a handwritten label, with students divorcing this laboratory activity from the entire dispensing [...] Read more.

A core competency of Australian Pharmacy graduates is to prepare and compound extemporaneous formulations. Students in our pharmacy course would traditionally formulate extemporaneous products in laboratory classes while simultaneously preparing a handwritten label, with students divorcing this laboratory activity from the entire dispensing process. As a way to incorporate the dispensing process into the preparation of extemporaneous products without adding excessive time to the laboratory, we integrated MyDispense, a virtual pharmacy simulation, in pre-laboratory activities. This meant that students could complete all the dispensing activities for prescribed extemporaneous formulations prior to attending the laboratory. Prescriptions for solutions, suspensions, creams and ointments were developed in MyDispense, including essential components for dispensing an extemporaneous formulation (e.g., formulation name, dosing instructions). These prescriptions were provided to students at least 1 week prior to their laboratory classes, whereas for the laboratory assessments, the prescription was provided at the commencement of the extemporaneous exam. Due to the implementation of dispensing via MyDispense, we found that students demonstrated pre-laboratory engagement as all students presented their printed labels upon entering the laboratory. We also observed an increase in interaction between students and laboratory facilitators, mainly focused on the principles of formulation integrated around patient outcomes. Virtual simulations such as MyDispense can therefore provide a guided realistic learning experience, whilst overcoming time pressures associated with laboratory timetabling. This approach also encourages students to engage in the dispensing process prior to extemporaneous laboratories providing more opportunity to discuss higher-level formulation principles and patient-centred outcomes during laboratory classes. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

27 pages, 3115 KiB

Open AccessSystematic Review

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

by Andrej Thurzo, Wanda Urbanová, Bohuslav Novák, Ladislav Czako, Tomáš Siebert, Peter Stano, Simona Mareková, Georgia Fountoulaki, Helena Kosnáčová and Ivan Varga

Healthcare 2022, 10(7), 1269; https://doi.org/10.3390/healthcare10071269 - 08 Jul 2022

Cited by 42 | Viewed by 10809

Abstract

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and [...] Read more.

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)

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