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Electronics
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Medical Applications of Artificial Intelligence
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Medical Applications of Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4809

Special Issue Editors

Prof. Dr. Jawahar (Jay) Kalra

E-Mail Website
Guest Editor
Pathology and Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E, Canada
Interests: quality care and patient safety; laboratory utilization in healthcare; medical error & disclosure of adverse events; total quality management in Laboratory medicine; artificial intelligence in healthcare; ethical innovation in medicine
Dr. Patrick Seitzinger

E-Mail Website
Guest Editor
Department of Pediatrics, University of Saskatchewan, Saskatoon, SK S7N 5E, Canada
Interests: healthcare innovation; artificial intelligence; healthcare quality and improvement

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is having a transformative effect on the healthcare delivery in unprecedented ways. AI is revolutionizing the way medical professionals approach patient care, from early diagnosis to personalized treatments. The use of AI in medical applications ranges from image analysis and diagnosis to predictive analytics, clinical decision support systems, and data management. With the rapidly advancing capabilities, AI is helping manage health data information to make it more accessible to patients, medical professionals, and hospital administrators. AI is also influencing areas of telemedicine and remote patient monitoring. The integration of treatment and rehabilitation has shown promise and potential in improving the accuracy and speed of procedures while reducing the risk of human error. However, while the potential of AI in healthcare is vast, there are also concerns about ethical implications and consequences. It is important that AI is developed and deployed with a strong focus on patient-centred care and safety. Healthcare has unique requirements which add additional complexity and challenges to the field of AI implementation. This Special Issue of Electronics focuses on the novel advancements in medical applications of artificial intelligence, exploring the opportunities and challenges of this rapidly evolving field of healthcare delivery. With AI becoming an increasingly integral component of healthcare utilization, it is essential to continue this critical discussion and idea sharing about the role of AI in medicine and healthcare.

Subsections:

  • Predictive analytics in disease management
  • Telemedicine and health equity
  • AI in health promotion and prevention and disease
  • Personalized medicine and precision health
  • AI in drug discovery and development
  • AI in global health and disaster response
  • AI in epidemiology and public health
  • Ethical considerations in AI in healthcare
  • Future developments and trends in medical AI

Prof. Dr. Jawahar (Jay) Kalra
Dr. Patrick Seitzinger
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. Electronics 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 2400 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

  • artificial intelligence
  • medicine
  • healthcare improvement
  • medical innovation

Published Papers (5 papers)

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Research

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17 pages, 971 KiB  
Article
UTAC-Net: A Semantic Segmentation Model for Computer-Aided Diagnosis for Ischemic Region Based on Nuclear Medicine Cerebral Perfusion Imaging
by Wangxiao Li and Wei Zhang
Electronics 2024, 13(8), 1466; https://doi.org/10.3390/electronics13081466 - 12 Apr 2024
Viewed by 286
Abstract
Cerebral ischemia has a high morbidity and disability rate. Clinical diagnosis is mainly made by radiologists manually reviewing cerebral perfusion images to determine whether cerebral ischemia is present. The number of patients with cerebral ischemia has risen dramatically in recent years, which has [...] Read more.
Cerebral ischemia has a high morbidity and disability rate. Clinical diagnosis is mainly made by radiologists manually reviewing cerebral perfusion images to determine whether cerebral ischemia is present. The number of patients with cerebral ischemia has risen dramatically in recent years, which has brought a huge workload for radiologists. In order to improve the efficiency of diagnosis, we develop a neural network for segmenting cerebral ischemia regions in perfusion images. Combining deep learning with medical imaging technology, we propose a segmentation network, UTAC-Net, based on U-Net and Transformer, which includes a contour-aware module and an attention branching fusion module, to achieve accurate segmentation of cerebral ischemic regions and correct identification of ischemic locations. Cerebral ischemia datasets are scarce, so we built a relevant dataset. The results on the self-built dataset show that UTAC-Net is superior to other networks, with the mDice of UTAC-Net increasing by 9.16% and mIoU increasing by 14.06% compared with U-Net. The output results meet the needs of aided diagnosis as judged by radiologists. Experiments have demonstrated that our algorithm has higher segmentation accuracy than other algorithms and better assists radiologists in the initial diagnosis, thereby reducing radiologists’ workload and improving diagnostic efficiency. Full article
(This article belongs to the Special Issue Medical Applications of Artificial Intelligence)
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19 pages, 3713 KiB  
Article
Development of AI-Based Prediction of Heart Attack Risk as an Element of Preventive Medicine
by Izabela Rojek, Piotr Kotlarz, Mirosław Kozielski, Mieczysław Jagodziński and Zbyszko Królikowski
Electronics 2024, 13(2), 272; https://doi.org/10.3390/electronics13020272 - 07 Jan 2024
Viewed by 1503
Abstract
The future paradigm of early cardiac diagnostics is shifting the focus towards heart attack preventive medicine based on non-invasive medical imaging with the support of artificial intelligence. It is necessary to preventively detect its increased risk early and respond with preventive drugs before [...] Read more.
The future paradigm of early cardiac diagnostics is shifting the focus towards heart attack preventive medicine based on non-invasive medical imaging with the support of artificial intelligence. It is necessary to preventively detect its increased risk early and respond with preventive drugs before moving on to more effective, but also more invasive, forms of therapy. The main motivation of our study was to improve existing and develop new AI-based solutions for cardiac preventive medicine, with particular emphasis on the prevention of heart attacks. This is due to the fact that the epidemic of lifestyle diseases (including cardiologic ones) has been stopped but not reversed; hence, automatically supervised prevention using AI seems to be a key opportunity to introduce progress in the above-mentioned areas. This can have major effects not only scientific and clinical in nature, but also economic and social. The aim of this article is to develop and test an AI-based tool designed to predict the occurrence of a heart attack for the purposes of preventive medicine. It used the combination and comparison of multiple AI methods and techniques to determine a personalized heart attack probability based on a wide range of patient characteristics and, from a computational point of view, determine the minimum set of characteristics necessary to do so. When applied to a specific patient, this represents progress in this field of research, resulting in improvements in preclinical care and diagnostics, as well as predictive accuracy in preventive medicine. After an initial selection based on the authors’ knowledge and experience, four solutions turned out to be the best: linear support vector machine (Linear SVC), logistic regression, k-nearest neighbors algorithm (KNN, k-NN), and random forest. A comparison of the models developed in the study shows that models based on logistic regression proved to be the most accurate, although their predictive value is moderate, but sufficient for the initial screening diagnosis—selecting patients who require further, more accurate testing. In addition, this can be performed based on a reduced set of parameters, particularly heart rate, age, BMI, and cholesterol. This allows the development of a prevention strategy based on modifiable factors (e.g., in the form of diet, activity modification, or a hybrid combining different factors) combined with the monitoring of heart attack risk by the proposed system. The novelty and contribution of the described system lies in the use of AI for a widely available, cheap, and quick predictive analysis of cardiovascular functions in a group of patients classified as at risk, and over time in all patients as a standard periodic examination qualifying them for further, more advanced diagnosis of heart diseases. Full article
(This article belongs to the Special Issue Medical Applications of Artificial Intelligence)
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18 pages, 8690 KiB  
Article
Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification
by Pendar Alirezazadeh, Fadi Dornaika and Abdelmalik Moujahid
Electronics 2023, 12(20), 4356; https://doi.org/10.3390/electronics12204356 - 20 Oct 2023
Viewed by 827
Abstract
When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology [...] Read more.
When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology diagnoses and reducing the analysis time. Convolutional neural networks (CNNs) have recently gained traction for their ability to more accurately classify histopathological breast cancer images. CNNs excel at extracting distinctive features that emphasize semantic information. However, traditional CNNs employing the softmax loss function often struggle to achieve the necessary discriminatory power for this task. To address this challenge, a set of angular margin-based softmax loss functions have emerged, including angular softmax (A-Softmax), large margin cosine loss (CosFace), and additive angular margin (ArcFace), each sharing a common objective: maximizing inter-class variation while minimizing intra-class variation. This study delves into these three loss functions and their potential to extract distinguishing features while expanding the decision boundary between classes. Rigorous experimentation on a well-established histopathological breast cancer image dataset, BreakHis, has been conducted. As per the results, it is evident that CosFace focuses on augmenting the differences between classes, while A-Softmax and ArcFace tend to emphasize augmenting within-class variations. These observations underscore the efficacy of margin penalties on angular softmax losses in enhancing feature discrimination within the embedding space. These loss functions consistently outperform softmax-based techniques, either by widening the gaps among classes or enhancing the compactness of individual classes. Full article
(This article belongs to the Special Issue Medical Applications of Artificial Intelligence)
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18 pages, 8405 KiB  
Article
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features’ Learning Efficacy
by Hyunsu Lee
Electronics 2023, 12(20), 4212; https://doi.org/10.3390/electronics12204212 - 11 Oct 2023
Viewed by 571
Abstract
The focus of this study is to investigate the impact of different initialization strategies for the weight matrix of Successor Features (SF) on the learning efficiency and convergence in Reinforcement Learning (RL) agents. Using a grid-world paradigm, we compare the performance of RL [...] Read more.
The focus of this study is to investigate the impact of different initialization strategies for the weight matrix of Successor Features (SF) on the learning efficiency and convergence in Reinforcement Learning (RL) agents. Using a grid-world paradigm, we compare the performance of RL agents, whose SF weight matrix is initialized with either an identity matrix, zero matrix, or a randomly generated matrix (using the Xavier, He, or uniform distribution method). Our analysis revolves around evaluating metrics such as the value error, step length, PCA of Successor Representation (SR) place field, and the distance of the SR matrices between different agents. The results demonstrate that the RL agents initialized with random matrices reach the optimal SR place field faster and showcase a quicker reduction in value error, pointing to more efficient learning. Furthermore, these random agents also exhibit a faster decrease in step length across larger grid-world environments. The study provides insights into the neurobiological interpretations of these results, their implications for understanding intelligence, and potential future research directions. These findings could have profound implications for the field of artificial intelligence, particularly in the design of learning algorithms. Full article
(This article belongs to the Special Issue Medical Applications of Artificial Intelligence)
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Review

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14 pages, 1749 KiB  
Review
Artificial Intelligence and Pediatrics: Synthetic Knowledge Synthesis
by Jernej Završnik, Peter Kokol, Bojan Žlahtič and Helena Blažun Vošner
Electronics 2024, 13(3), 512; https://doi.org/10.3390/electronics13030512 - 26 Jan 2024
Viewed by 1047
Abstract
The first publication on the use of artificial intelligence (AI) in pediatrics dates back to 1984. Since then, research on AI in pediatrics has become much more popular, and the number of publications has largely increased. Consequently, a need for a holistic research [...] Read more.
The first publication on the use of artificial intelligence (AI) in pediatrics dates back to 1984. Since then, research on AI in pediatrics has become much more popular, and the number of publications has largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into the use of AI in pediatrics has arisen. To fill this gap, a novel methodology, synthetic knowledge synthesis (SKS), was applied. Using SKS, we identified the most prolific countries, institutions, source titles, funding agencies, and research themes and the most frequently used AI algorithms and their applications in pediatrics. The corpus was extracted from the Scopus (Elsevier, The Netherlands) bibliographic database and analyzed using VOSViewer, version 1.6.20. Done An exponential growth in the literature was observed in the last decade. The United States, China, and Canada were the most productive countries. Deep learning was the most used machine learning algorithm and classification, and natural language processing was the most popular AI approach. Pneumonia, epilepsy, and asthma were the most targeted pediatric diagnoses, and prediction and clinical decision making were the most frequent applications. Full article
(This article belongs to the Special Issue Medical Applications of Artificial Intelligence)
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