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Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts

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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 (31 August 2023) | Viewed by 25641

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Prof. Dr. Semen A. Kurkin

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Guest Editor

Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
Interests: neuroscience; neuroimaging; artificial intelligence in medicine; EEG, MEG, and fMRI; brain–computer interfaces; neurodegenerative diseases; neurorehabilitation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Alexander E. Hramov

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Guest Editor

1. Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia
2. Neurosciences Research Institute of Samara State Medical University, Samara 443079, Russia
3. Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan 420500, Russia
Interests: neuroscience; nonlinear dynamics; wavelets; intelligent systems; synchronization; biomedical signal processing; neuronal networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is a state-of-the-art computational tool employed for analyzing big data in fundamental and applied science. Recently, it has gained popularity in medicine as an assistive technology that supports health research and medical practices, including treatment, recovery, disease prevention, and health promotion for individuals and whole populations. The digital tools of modern medicine will enable a breakthrough in the very approach to treating and preventing disease. Currently, as part of a personalized concept, we must look for biomarkers of patient conditions and try to correlate them with the prognosis of a patient's health status or the course of the disease. The rapid development of AI technologies suggests that the ambitious goal formulated can be achieved by applying AI concepts. This Special Issue aims to attract high-quality research studies and reviews from scholars, professors, researchers, and engineers that advance the application of state-of-the-art artificial intelligence concepts in medical diagnostic systems. Potential areas of interest include, but are not limited to, the following directions:

Explainable AI and deep learning in medicine;
Diagnostics with smart wearable devices (healthcare gadgets);
In vitro diagnostics with AI applications;
Medical images analysis and diagnostics;
Biomedical signals processing;
Models and systems for AI-based public health;
Data analytics and mining for clinical decision support systems.

The speakers' submissions of "Baltic Forum: Neuroscience, Artificial Intelligence and Complex Systems", contributing to our objective to develop medical diagnostic systems based on advancing AI concepts, are strongly welcomed for the Special Issue.

Prof. Dr. Semen A. Kurkin
Prof. Dr. Alexander E. Hramov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

Keywords

explainable artificial intelligence
neuroscience
healthcare gadgets
biosensors and biochips
biomedical signals processing
clinical decision support systems
diagnostics
machine learning
precision medicine
data analytics and mining in medicine

Published Papers (11 papers)

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Research

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27 pages, 1029 KiB

Open AccessArticle

Combining Gaussian Process with Hybrid Optimal Feature Decision in Cuffless Blood Pressure Estimation

by Soojeong Lee, Gyanendra Prasad Joshi, Chang-Hwan Son and Gangseong Lee

Diagnostics 2023, 13(4), 736; https://doi.org/10.3390/diagnostics13040736 - 15 Feb 2023

Cited by 1 | Viewed by 1408

Abstract

Noninvasive blood pressure estimation is crucial for cardiovascular and hypertension patients. Cuffless-based blood pressure estimation has received much attention recently for continuous blood pressure monitoring. This paper proposes a new methodology that combines the Gaussian process with hybrid optimal feature decision (HOFD) in cuffless blood pressure estimation. First, we can choose one of the feature selection methods: robust neighbor component analysis (RNCA), minimum redundancy, maximum relevance (MRMR), and F-test, based on the proposed hybrid optimal feature decision. After that, a filter-based RNCA algorithm uses the training dataset to obtain weighted functions by minimizing the loss function. Next, we combine the Gaussian process (GP) algorithm as the evaluation criteria, which is used to determine the best feature subset. Hence, combining GP with HOFD leads to an effective feature selection process. The proposed combining Gaussian process with the RNCA algorithm shows that the root mean square errors (RMSEs) for the SBP (10.75 mmHg) and DBP (8.02 mmHg) are lower than those of the conventional algorithms. The experimental results represent that the proposed algorithm is very effective. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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15 pages, 2418 KiB

Open AccessArticle

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

by Vadi Su Yilmaz, Metehan Akdag, Yaser Dalveren, Resat Ozgur Doruk, Ali Kara and Ahmet Soylu

Diagnostics 2023, 13(4), 651; https://doi.org/10.3390/diagnostics13040651 - 09 Feb 2023

Viewed by 1518

Abstract

Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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18 pages, 2449 KiB

Open AccessArticle

Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors’ Functional Outcome upon Discharge Utilizing Explainable Machine Learning

by Aimilios Gkantzios, Christos Kokkotis, Dimitrios Tsiptsios, Serafeim Moustakidis, Elena Gkartzonika, Theodoros Avramidis, Nikolaos Aggelousis and Konstantinos Vadikolias

Diagnostics 2023, 13(3), 532; https://doi.org/10.3390/diagnostics13030532 - 01 Feb 2023

Cited by 7 | Viewed by 2021

Abstract

Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: “Independent” vs. “Non-Independent” and “Non-Disability” vs. “Disability”. Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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15 pages, 600 KiB

Open AccessArticle

Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning

by Romina Torres, Christopher Zurita, Diego Mellado, Orietta Nicolis, Carolina Saavedra, Marcelo Tuesta, Matías Salinas, Ayleen Bertini, Oneglio Pedemonte, Marvin Querales and Rodrigo Salas

Diagnostics 2023, 13(3), 508; https://doi.org/10.3390/diagnostics13030508 - 30 Jan 2023

Viewed by 1690

Abstract

Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of

0.03 \pm 0.013

and an

R^{2}

63 \pm 19 %

, where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an

R^{2}

up to

92 %

. The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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10 pages, 872 KiB

Open AccessArticle

Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series

by Alexander A. Koronovskii, Jr., Inna A. Blokhina, Alexander V. Dmitrenko, Matvey A. Tuzhilkin, Tatyana V. Moiseikina, Inna V. Elizarova, Oxana V. Semyachkina-Glushkovskaya and Alexey N. Pavlov

Diagnostics 2023, 13(1), 93; https://doi.org/10.3390/diagnostics13010093 - 28 Dec 2022

Viewed by 1034

Abstract

A coarse-graining procedure, which involves averaging time series in non-overlapping windows followed by processing of the obtained multiple data sets, is the initial step in the multiscale entropy computation method. In this paper, we discuss how this procedure can be applied with other methods of time series analysis. Based on extended detrended fluctuation analysis (EDFA), we compare signal processing results for data sets with and without coarse-graining. Using the simulated data provided by the interacting nephrons model, we show how this procedure increases, up to 48%, the distinctions between local scaling exponents quantifying synchronous and asynchronous chaotic oscillations. Based on the experimental data of electrocorticograms (ECoG) of mice, an improvement in differences in local scaling exponents up to 41% and Student’s t-values up to 34% was revealed. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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30 pages, 3615 KiB

Open AccessArticle

ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images

by Aymen M. Al-Hejri, Riyadh M. Al-Tam, Muneer Fazea, Archana Harsing Sable, Soojeong Lee and Mugahed A. Al-antari

Diagnostics 2023, 13(1), 89; https://doi.org/10.3390/diagnostics13010089 - 28 Dec 2022

Cited by 14 | Viewed by 4001

Abstract

Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: Approach A (i.e., binary classification) and Approach B (i.e., multi-classification). To build the proposed CAD system, the benchmark public multi-class INbreast dataset is used. Meanwhile, private real breast cancer images are collected and annotated by expert radiologists to validate the prediction performance of the proposed ETECADx framework. The promising evaluation results are achieved using the INbreast mammograms with overall accuracies of 98.58% and 97.87% for the binary and multi-class approaches, respectively. Compared with the individual backbone networks, the proposed ensemble learning model improves the breast cancer prediction performance by 6.6% for binary and 4.6% for multi-class approaches. The proposed hybrid ETECADx shows further prediction improvement when the ViT-based ensemble backbone network is used by 8.1% and 6.2% for binary and multi-class diagnosis, respectively. For validation purposes using the real breast images, the proposed CAD system provides encouraging prediction accuracies of 97.16% for binary and 89.40% for multi-class approaches. The ETECADx has a capability to predict the breast lesions for a single mammogram in an average of 0.048 s. Such promising performance could be useful and helpful to assist the practical CAD framework applications providing a second supporting opinion of distinguishing various breast cancer malignancies. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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16 pages, 466 KiB

Open AccessArticle

Computational Evidence for Laboratory Diagnostic Pathways: Extracting Predictive Analytes for Myocardial Ischemia from Routine Hospital Data

by Zara Liniger, Benjamin Ellenberger and Alexander Benedikt Leichtle

Diagnostics 2022, 12(12), 3148; https://doi.org/10.3390/diagnostics12123148 - 13 Dec 2022

Cited by 1 | Viewed by 1630

Abstract

Background: Laboratory parameters are critical parts of many diagnostic pathways, mortality scores, patient follow-ups, and overall patient care, and should therefore have underlying standardized, evidence-based recommendations. Currently, laboratory parameters and their significance are treated differently depending on expert opinions, clinical environment, and varying hospital guidelines. In our study, we aimed to demonstrate the capability of a set of algorithms to identify predictive analytes for a specific diagnosis. As an illustration of our proposed methodology, we examined the analytes associated with myocardial ischemia; it was a well-researched diagnosis and provides a substrate for comparison. We intend to present a toolset that will boost the evolution of evidence-based laboratory diagnostics and, therefore, improve patient care. Methods: The data we used consisted of preexisting, anonymized recordings from the emergency ward involving all patient cases with a measured value for troponin T. We used multiple imputation technique, orthogonal data augmentation, and Bayesian Model Averaging to create predictive models for myocardial ischemia. Each model incorporated different analytes as cofactors. In examining these models further, we could then conclude the predictive importance of each analyte in question. Results: The used algorithms extracted troponin T as a highly predictive analyte for myocardial ischemia. As this is a known relationship, we saw the predictive importance of troponin T as a proof of concept, suggesting a functioning method. Additionally, we could demonstrate the algorithm’s capabilities to extract known risk factors of myocardial ischemia from the data. Conclusion: In this pilot study, we chose an assembly of algorithms to analyze the value of analytes in predicting myocardial ischemia. By providing reliable correlations between the analytes and the diagnosis of myocardial ischemia, we demonstrated the possibilities to create unbiased computational-based guidelines for laboratory diagnostics by using computational power in today’s era of digitalization. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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13 pages, 1570 KiB

Open AccessArticle

Clinical Evaluation of the ButterfLife Device for Simultaneous Multiparameter Telemonitoring in Hospital and Home Settings

by Francesco Salton, Stefano Kette, Paola Confalonieri, Sergio Fonda, Selene Lerda, Michael Hughes, Marco Confalonieri and Barbara Ruaro

Diagnostics 2022, 12(12), 3115; https://doi.org/10.3390/diagnostics12123115 - 10 Dec 2022

Viewed by 1622

Abstract

We conducted a two-phase study to test the reliability and usability of an all-in-one artificial intelligence-based device (ButterfLife), which allows simultaneous monitoring of five vital signs. The first phase of the study aimed to test the agreement between measurements performed with ButterfLife vs. standard of care (SoC) in 42 hospitalized patients affected by acute respiratory failure. In this setting, the greatest discordance between ButterfLife and SoC was in respiratory rate (mean difference −4.69 bpm). Significantly close correlations were observed for all parameters except diastolic blood pressure and oxygen saturation (Spearman’s Rho −0.18 mmHg; p = 0.33 and 0.20%; p = 0.24, respectively). The second phase of the study was conducted on eight poly-comorbid patients using ButterfLife at home, to evaluate the number of clinical conditions detected, as well as the patients’ compliance and satisfaction. The average proportion of performed tests compared with the scheduled number was 67.4%, and no patients reported difficulties with use. Seven conditions requiring medical attention were identified, with a sensitivity of 100% and specificity of 88.9%. The median patient satisfaction was 9.5/10. In conclusion, ButterfLife proved to be a reliable and easy-to-use device, capable of simultaneously assessing five vital signs in both hospital and home settings. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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Review

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15 pages, 553 KiB

Open AccessReview

Prevention Strategies and Early Diagnosis of Cervical Cancer: Current State and Prospects

by Viktor V. Kakotkin, Ekaterina V. Semina, Tatiana G. Zadorkina and Mikhail A. Agapov

Diagnostics 2023, 13(4), 610; https://doi.org/10.3390/diagnostics13040610 - 07 Feb 2023

Cited by 3 | Viewed by 2278

Abstract

Cervical cancer ranks third among all new cancer cases and causes of cancer deaths in females. The paper provides an overview of cervical cancer prevention strategies employed in different regions, with incidence and mortality rates ranging from high to low. It assesses the effectiveness of approaches proposed by national healthcare systems by analysing data published in the National Library of Medicine (Pubmed) since 2018 featuring the following keywords: “cervical cancer prevention”, “cervical cancer screening”, “barriers to cervical cancer prevention”, “premalignant cervical lesions” and “current strategies”. WHO’s 90-70-90 global strategy for cervical cancer prevention and early screening has proven effective in different countries in both mathematical models and clinical practice. The data analysis carried out within this study identified promising approaches to cervical cancer screening and prevention, which can further enhance the effectiveness of the existing WHO strategy and national healthcare systems. One such approach is the application of AI technologies for detecting precancerous cervical lesions and choosing treatment strategies. As such studies show, the use of AI can not only increase detection accuracy but also ease the burden on primary care. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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27 pages, 1566 KiB

Open AccessReview

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

by Natalia Shusharina, Denis Yukhnenko, Stepan Botman, Viktor Sapunov, Vladimir Savinov, Gleb Kamyshov, Dmitry Sayapin and Igor Voznyuk

Diagnostics 2023, 13(3), 573; https://doi.org/10.3390/diagnostics13030573 - 03 Feb 2023

Cited by 9 | Viewed by 3696

Abstract

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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35 pages, 5766 KiB

Open AccessReview

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

by Xiao Jian Tan, Wai Loon Cheor, Li Li Lim, Khairul Shakir Ab Rahman and Ikmal Hisyam Bakrin

Diagnostics 2022, 12(12), 3111; https://doi.org/10.3390/diagnostics12123111 - 09 Dec 2022

Cited by 6 | Viewed by 3225

Abstract

Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest. Full article

(This article belongs to the Special Issue Medical Diagnostic Systems Based on Advancing Artificial Intelligence Concepts)

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