Applications of Artificial Intelligence in Healthcare Monitoring

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13412

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Guest Editor
School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India
Interests: machine learning; communication systems and networks; multimedia and computer vision; artificial intelligence; data science; wireless networks
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on both the theoretical and practical aspects of healthcare monitoring applications based on artificial intelligence methods. The following topics are welcomed but not limited to:

  • Medical diagnosis;
  • Artificial intelligence, deep learning, machine learning;
  • Intelligent patient monitoring systems;
  • Wearable intelligent sensors for health applications;
  • Remote diagnostic support systems;
  • Preand post-treatment monitoring systems;
  • Condition monitoring for elderly people.

Dr. Kathiravan Srinivasan
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

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13 pages, 2898 KiB  
Article
Immunohistochemical Stain-Aided Annotation Accelerates Machine Learning and Deep Learning Model Development in the Pathologic Diagnosis of Nasopharyngeal Carcinoma
by Tai-Pei Lin, Chiou-Ying Yang, Ko-Jiunn Liu, Meng-Yuan Huang and Yen-Lin Chen
Diagnostics 2023, 13(24), 3685; https://doi.org/10.3390/diagnostics13243685 - 18 Dec 2023
Viewed by 1025
Abstract
Nasopharyngeal carcinoma (NPC) is an epithelial cancer originating in the nasopharynx epithelium. Nevertheless, annotating pathology slides remains a bottleneck in the development of AI-driven pathology models and applications. In the present study, we aim to demonstrate the feasibility of using immunohistochemistry (IHC) for [...] Read more.
Nasopharyngeal carcinoma (NPC) is an epithelial cancer originating in the nasopharynx epithelium. Nevertheless, annotating pathology slides remains a bottleneck in the development of AI-driven pathology models and applications. In the present study, we aim to demonstrate the feasibility of using immunohistochemistry (IHC) for annotation by non-pathologists and to develop an efficient model for distinguishing NPC without the time-consuming involvement of pathologists. For this study, we gathered NPC slides from 251 different patients, comprising hematoxylin and eosin (H&E) slides, pan-cytokeratin (Pan-CK) IHC slides, and Epstein–Barr virus-encoded small RNA (EBER) slides. The annotation of NPC regions in the H&E slides was carried out by a non-pathologist trainee who had access to corresponding Pan-CK IHC slides, both with and without EBER slides. The training process utilized ResNeXt, a deep neural network featuring a residual and inception architecture. In the validation set, NPC exhibited an AUC of 0.896, with a sensitivity of 0.919 and a specificity of 0.878. This study represents a significant breakthrough: the successful application of deep convolutional neural networks to identify NPC without the need for expert pathologist annotations. Our results underscore the potential of laboratory techniques to substantially reduce the workload of pathologists. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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16 pages, 6573 KiB  
Article
Auditory Brainstem Response Data Preprocessing Method for the Automatic Classification of Hearing Loss Patients
by Jun Ma, Jae-Hyun Seo, Il Joon Moon, Moo Kyun Park, Jong Bin Lee, Hantai Kim, Joong Ho Ahn, Jeong Hun Jang, Jong Dae Lee, Seong Jun Choi and Min Hong
Diagnostics 2023, 13(23), 3538; https://doi.org/10.3390/diagnostics13233538 - 27 Nov 2023
Viewed by 1019
Abstract
Auditory brainstem response (ABR) is the response of the brain stem through the auditory nerve. The ABR test is a method of testing for loss of hearing through electrical signals. Basically, the test is conducted on patients such as the elderly, the disabled, [...] Read more.
Auditory brainstem response (ABR) is the response of the brain stem through the auditory nerve. The ABR test is a method of testing for loss of hearing through electrical signals. Basically, the test is conducted on patients such as the elderly, the disabled, and infants who have difficulty in communication. This test has the advantage of being able to determine the presence or absence of objective hearing loss by brain stem reactions only, without any communication. This paper proposes the image preprocessing process required to construct an efficient graph image data set for deep learning models using auditory brainstem response data. To improve the performance of the deep learning model, we standardized the ABR image data measured on various devices with different forms. In addition, we applied the VGG16 model, a CNN-based deep learning network model developed by a research team at the University of Oxford, using preprocessed ABR data to classify the presence or absence of hearing loss and analyzed the accuracy of the proposed method. This experimental test was performed using 10,000 preprocessed data, and the model was tested with various weights to verify classification learning. Based on the learning results, we believe it is possible to help set the criteria for preprocessing and the learning process in medical graph data, including ABR graph data. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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24 pages, 4900 KiB  
Article
Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework
by Ahmed M. Elshewey, Mahmoud Y. Shams, Sayed M. Tawfeek, Amal H. Alharbi, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Marwa M. Eid, Nima Khodadadi, Laith Abualigah, Doaa Sami Khafaga and Zahraa Tarek
Diagnostics 2023, 13(22), 3439; https://doi.org/10.3390/diagnostics13223439 - 13 Nov 2023
Viewed by 1087
Abstract
The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical [...] Read more.
The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of the highest rates of HCV in the world. The high prevalence is linked to several factors, including the use of injection drugs, poor sterilization practices in medical facilities, and low public awareness. This paper introduces a hyOPTGB model, which employs an optimized gradient boosting (GB) classifier to predict HCV disease in Egypt. The model’s accuracy is enhanced by optimizing hyperparameters with the OPTUNA framework. Min-Max normalization is used as a preprocessing step for scaling the dataset values and using the forward selection (FS) wrapped method to identify essential features. The dataset used in the study contains 1385 instances and 29 features and is available at the UCI machine learning repository. The authors compare the performance of five machine learning models, including decision tree (DT), support vector machine (SVM), dummy classifier (DC), ridge classifier (RC), and bagging classifier (BC), with the hyOPTGB model. The system’s efficacy is assessed using various metrics, including accuracy, recall, precision, and F1-score. The hyOPTGB model outperformed the other machine learning models, achieving a 95.3% accuracy rate. The authors also compared the hyOPTGB model against other models proposed by authors who used the same dataset. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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13 pages, 1455 KiB  
Article
Assessment of UTI Diagnostic Techniques Using the Fuzzy–PROMETHEE Model
by Mariam Abobakr, Berna Uzun, Dilber Uzun Ozsahin, Tamer Sanlidag and Ayse Arikan
Diagnostics 2023, 13(22), 3421; https://doi.org/10.3390/diagnostics13223421 - 10 Nov 2023
Cited by 1 | Viewed by 1004
Abstract
Accurate diagnosis of urinary tract infections (UTIs) is important as early diagnosis increases treatment rates, reduces the risk of infection and disease spread, and prevents deaths. This study aims to evaluate various parameters of existing and developing techniques for the diagnosis of UTIs, [...] Read more.
Accurate diagnosis of urinary tract infections (UTIs) is important as early diagnosis increases treatment rates, reduces the risk of infection and disease spread, and prevents deaths. This study aims to evaluate various parameters of existing and developing techniques for the diagnosis of UTIs, the majority of which are approved by the FDA, and rank them according to their performance levels. The study includes 16 UTI tests, and the fuzzy preference ranking organization method was used to analyze the parameters such as analytical efficiency, result time, specificity, sensitivity, positive predictive value, and negative predictive value. Our findings show that the biosensor test was the most indicative of expected test performance for UTIs, with a net flow of 0.0063. This was followed by real-time microscopy systems, catalase, and combined LE and nitrite, which were ranked second, third, and fourth with net flows of 0.003, 0.0026, and 0.0025, respectively. Sequence-based diagnostics was the least favourable alternative with a net flow of −0.0048. The F–PROMETHEE method can aid decision makers in making decisions on the most suitable UTI tests to support the outcomes of each country or patient based on specific conditions and priorities. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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22 pages, 3887 KiB  
Article
Application of Machine Learning Strategies to Model the Effects of Sevoflurane on Somatosensory-Evoked Potentials during Spine Surgery
by John Preston Wilson, Jr., Deepak Kumbhare, Charles Ronkon, Bharat Guthikonda and Stanley Hoang
Diagnostics 2023, 13(21), 3389; https://doi.org/10.3390/diagnostics13213389 - 06 Nov 2023
Viewed by 883
Abstract
In this study, a small sample of patients’ neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during [...] Read more.
In this study, a small sample of patients’ neuromonitoring data was analyzed using machine learning (ML) tools to provide proof of concept for quantifying complex signals. Intraoperative neurophysiological monitoring (IONM) is a valuable asset for monitoring the neurological status of a patient during spine surgery. Notably, this technology, when operated by neurophysiologists and surgeons familiar with proper alarm criteria, is capable of detecting neurological deficits. However, non-surgical factors, such as volatile anesthetics like sevoflurane, can negatively influence robust IONM signal generation. While sevoflurane has been shown to affect the latency and amplitude of somatosensory evoked potential (SSEP), a more complex and nuanced analysis of the SSEP waveform has not been performed. In this study, signal processing and machine learning techniques were used to more intricately characterize and predict SSEP waveform changes as a function of varying end-tidal sevoflurane concentration. With data from ten patients who underwent spinal procedures, features describing the SSEP waveforms were generated using principal component analysis (PCA), phase space curves (PSC), and time-frequency analysis (TFA). A minimum redundancy maximum relevance (MRMR) feature selection technique was then used to identify the most important SSEP features associated with changing sevoflurane concentrations. Once the features carrying the maximum amount of information about the majority of signal waveform variability were identified, ML models were used to predict future changes in SSEP waveforms. Linear regression, regression trees, support vector machines, and neural network ML models were then selected for testing. Using SSEP data from eight patients, the models were trained using a range of features selected during MRMR calculations. During the training phase of model development, the highest performing models were identified as support vector machines and regression trees. After identifying the highest performing models for each nerve group, we tested these models using the remaining two patients’ data. We compared the models’ performance metrics using the root mean square error values (RMSEs). The feasibility of the methodology described provides a general framework for the applications of machine learning strategies to further delineate the effects of surgical and non-surgical factors affecting IONM signals. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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22 pages, 1480 KiB  
Article
Snake-Efficient Feature Selection-Based Framework for Precise Early Detection of Chronic Kidney Disease
by Walaa N. Ismail
Diagnostics 2023, 13(15), 2501; https://doi.org/10.3390/diagnostics13152501 - 27 Jul 2023
Cited by 3 | Viewed by 1058
Abstract
Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to detect CKD in its [...] Read more.
Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to detect CKD in its early stages. Manual screening is time-consuming and subject to personal judgment. Therefore, methods based on machine learning (ML) and automatic feature selection are used to support graders. The goal of feature selection is to identify the most relevant and informative subset of features in a given dataset. This approach helps mitigate the curse of dimensionality, reduce dimensionality, and enhance model performance. The use of natural-inspired optimization algorithms has been widely adopted to develop appropriate representations of complex problems by conducting a blackbox optimization process without explicitly formulating mathematical formulations. Recently, snake optimization algorithms have been developed to identify optimal or near-optimal solutions to difficult problems by mimicking the behavior of snakes during hunting. The objective of this paper is to develop a novel snake-optimized framework named CKD-SO for CKD data analysis. To select and classify the most suitable medical data, five machine learning algorithms are deployed, along with the snake optimization (SO) algorithm, to create an extremely accurate prediction of kidney and liver disease. The end result is a model that can detect CKD with 99.7% accuracy. These results contribute to our understanding of the medical data preparation pipeline. Furthermore, implementing this method will enable health systems to achieve effective CKD prevention by providing early interventions that reduce the high burden of CKD-related diseases and mortality. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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19 pages, 1696 KiB  
Article
Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System
by Abdullah M. Albarrak
Diagnostics 2023, 13(11), 1916; https://doi.org/10.3390/diagnostics13111916 - 30 May 2023
Cited by 1 | Viewed by 1064
Abstract
Medical data, such as electronic health records, are a repository for a patient’s medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient [...] Read more.
Medical data, such as electronic health records, are a repository for a patient’s medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data security. The introduction of visual analytics, a computing system that integrates analytics approaches with interactive visualizations, can potentially deal with information overload concerns in medical data. The practice of assessing the trustworthiness of visual analytics tools or applications using factors that affect medical data analysis is known as trustworthiness evaluation for medical data. It has a variety of major issues, such as a lack of important evaluation of medical data, the need to process much of medical data for diagnosis, the need to make trustworthy relationships clear, and the expectation that it will be automated. Decision-making strategies have been utilized in this evaluation process to avoid these concerns and intelligently and automatically analyze the trustworthiness of the visual analytics tool. The literature study found no hybrid decision support system for visual analytics tool trustworthiness in medical data diagnosis. Thus, this research develops a hybrid decision support system to assess and improve the trustworthiness of medical data for visual analytics tools using fuzzy decision systems. This study examined the trustworthiness of decision systems using visual analytics tools for medical data for the diagnosis of diseases. The hybrid multi-criteria decision-making-based decision support model, based on the analytic hierarchy process and sorting preferences by similarity to ideal solutions in a fuzzy environment, was employed in this study. The results were compared to highly correlated accuracy tests. In conclusion, we highlight the benefits of our proposed study, which includes performing a comparison analysis on the recommended models and some existing models in order to demonstrate the applicability of an optimal decision in real-world environments. In addition, we present a graphical interpretation of the proposed endeavor in order to demonstrate the coherence and effectiveness of our methodology. This research will also help medical experts select, evaluate, and rank the best visual analytics tools for medical data. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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32 pages, 16202 KiB  
Article
Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea
by Manuel Casal-Guisande, Laura Ceide-Sandoval, Mar Mosteiro-Añón, María Torres-Durán, Jorge Cerqueiro-Pequeño, José-Benito Bouza-Rodríguez, Alberto Fernández-Villar and Alberto Comesaña-Campos
Diagnostics 2023, 13(11), 1854; https://doi.org/10.3390/diagnostics13111854 - 25 May 2023
Cited by 3 | Viewed by 1824
Abstract
Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical [...] Read more.
Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient’s health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients’ condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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Review

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11 pages, 1181 KiB  
Review
The Potential Use of Artificial Intelligence in Irritable Bowel Syndrome Management
by Radu Alexandru Vulpoi, Mihaela Luca, Adrian Ciobanu, Andrei Olteanu, Oana Bărboi, Diana-Elena Iov, Loredana Nichita, Irina Ciortescu, Cristina Cijevschi Prelipcean, Gabriela Ștefănescu, Cătălina Mihai and Vasile Liviu Drug
Diagnostics 2023, 13(21), 3336; https://doi.org/10.3390/diagnostics13213336 - 29 Oct 2023
Cited by 1 | Viewed by 1531
Abstract
Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical [...] Read more.
Irritable bowel syndrome (IBS) has a global prevalence of around 4.1% and is associated with a low quality of life and increased healthcare costs. Current guidelines recommend that IBS is diagnosed using the symptom-based Rome IV criteria. Despite this, when patients seek medical attention, they are usually over-investigated. This issue might be resolved by novel technologies in medicine, such as the use of Artificial Intelligence (AI). In this context, this paper aims to review AI applications in IBS. AI in colonoscopy proved to be useful in organic lesion detection and diagnosis and in objectively assessing the quality of the procedure. Only a recently published study talked about the potential of AI-colonoscopy in IBS. AI was also used to study biofilm characteristics in the large bowel and establish a potential relationship with IBS. Moreover, an AI algorithm was developed in order to correlate specific bowel sounds with IBS. In addition to that, AI-based smartphone applications have been developed to facilitate the monitoring of IBS symptoms. From a therapeutic standpoint, an AI system was created to recommend specific diets based on an individual’s microbiota. In conclusion, future IBS diagnosis and treatment may benefit from AI. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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17 pages, 932 KiB  
Review
Beyond the Microscope: A Technological Overture for Cervical Cancer Detection
by Yong-Moon Lee, Boreom Lee, Nam-Hoon Cho and Jae Hyun Park
Diagnostics 2023, 13(19), 3079; https://doi.org/10.3390/diagnostics13193079 - 28 Sep 2023
Cited by 1 | Viewed by 1214
Abstract
Cervical cancer is a common and preventable disease that poses a significant threat to women’s health and well-being. It is the fourth most prevalent cancer among women worldwide, with approximately 604,000 new cases and 342,000 deaths in 2020, according to the World Health [...] Read more.
Cervical cancer is a common and preventable disease that poses a significant threat to women’s health and well-being. It is the fourth most prevalent cancer among women worldwide, with approximately 604,000 new cases and 342,000 deaths in 2020, according to the World Health Organization. Early detection and diagnosis of cervical cancer are crucial for reducing mortality and morbidity rates. The Papanicolaou smear test is a widely used screening method that involves the examination of cervical cells under a microscope to identify any abnormalities. However, this method is time-consuming, labor-intensive, subjective, and prone to human errors. Artificial intelligence techniques have emerged as a promising alternative to improve the accuracy and efficiency of Papanicolaou smear diagnosis. Artificial intelligence techniques can automatically analyze Papanicolaou smear images and classify them into normal or abnormal categories, as well as detect the severity and type of lesions. This paper provides a comprehensive review of the recent advances in artificial intelligence diagnostics of the Papanicolaou smear, focusing on the methods, datasets, performance metrics, and challenges. The paper also discusses the potential applications and future directions of artificial intelligence diagnostics of the Papanicolaou smear. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare Monitoring)
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