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Electronics
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Role of Artificial Intelligence in Healthcare and Biomedical Systems
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Role of Artificial Intelligence in Healthcare and Biomedical Systems

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 8530

Special Issue Editors

Dr. Mamoon Rashid

E-Mail Website
Guest Editor
Research Center of Excellence for Healthcare Informatics, Vishwakarma University, Pune, Maharashtra 411048, India
Interests: medical imaging; neuroimaging; healthcare informatics
Prof. Dr. Sultan S. Alshamrani

E-Mail Website
Guest Editor
Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
Interests: healthcare informatics; big data; machine learning; deep learning

Special Issue Information

Dear Colleagues,

The fields of healthcare informatics and biomedical engineering play an important role in the effectiveness of present-day healthcare systems. Advancement of Artificial Intelligence, Big Data Analytics, and Internet of Things technologies contribute greatly to various healthcare applications. Artificial Intelligence Techniques have replaced human-based systems and provided systems where prediction and diagnosis in healthcare diseases are quite accurate. The development of reliable and accurate healthcare models is possible with the help of machine learning and deep learning technologies. Artificial Intelligence has the power to solve many complex problems in the biomedical industry and is a technology that is supposed to decide the future of healthcare systems. Additionally, as cybersecurity threats to healthcare and biomedical applications grow in number and severity, artificial intelligence is helping providers detect vulnerabilities and respond to data breaches faster and with greater precision.

The track is open but not limited to following topics:

  • Artificial Intelligence in healthcare systems.
  • Use of machine learning in security of biomedical images.
  • Use of deep learning in security of biomedical images.
  • Use of Artificial intelligence in drug discovery.
  • AI-based prediction of diseases in medical images.
  • Medical image classification and analytics.
  • Artificial Intelligence in biomedical education.
  • Use of Artificial Intelligence in tele-diagnosis.
  • Use of Internet of Things and AI in securing biomedical systems.
  • Privacy and security issues in biomedical AI systems.
  • Healthcare and medical images security.
  • Security challenges in biomedical AI systems.
  • Future directions for use of AI and ML in securing healthcare applications.

Dr. Mamoon Rashid
Prof. Dr. Sultan S. Alshamrani
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

  • healthcare
  • medical imaging
  • biomedical
  • informatics
  • security

Published Papers (5 papers)

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Research

21 pages, 3462 KiB  
Article
DoseFormer: Dynamic Graph Transformer for Postoperative Pain Prediction
by Cao Zhang, Xiaohui Zhao, Ziyi Zhou, Xingyuan Liang and Shuai Wang
Electronics 2023, 12(16), 3507; https://doi.org/10.3390/electronics12163507 - 18 Aug 2023
Viewed by 768
Abstract
Many patients suffer from postoperative pain after surgery, which causes discomfort and influences recovery after the operation. During surgery, the anesthetists usually rely on their own experience when anesthetizing, which is not stable for avoiding postoperative pain. Hence, it is essential to predict [...] Read more.
Many patients suffer from postoperative pain after surgery, which causes discomfort and influences recovery after the operation. During surgery, the anesthetists usually rely on their own experience when anesthetizing, which is not stable for avoiding postoperative pain. Hence, it is essential to predict postoperative pain and give proper doses accordingly. Recently, the relevance of various clinical parameters and nociception has been investigated in many works, and several indices have been proposed for measuring the level of nociception. However, expensive advanced equipment is required when applying advanced medical technologies, which is not accessible to most institutions. In our work, we propose a deep learning model based on a dynamic graph transformer framework named DoseFormer to predict postoperative pain in a short period after an operation utilizing dynamic patient data recorded in existing widely utilized equipment (e.g., anesthesia monitor). DoseFormer consists of two modules: (i) We design a temporal model utilizing a long short-term memory (LSTM) model with an attention mechanism to capture dynamic intraoperative data of the patient and output a hybrid semantic embedding representing the patient information. (ii) We design a graph transformer network (GTN) to infer the postoperative pain level utilizing the relations across the patient embeddings. We evaluate the DoseFormer system with the medical records of over 999 patients undergoing cardiothoracic surgery in the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The experimental results show that our model achieves 92.16% accuracy for postoperative pain prediction and has a better comprehensive performance compared with baselines. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence in Healthcare and Biomedical Systems)
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26 pages, 22133 KiB  
Article
Fusion Model for Classification Performance Optimization in a Highly Imbalance Breast Cancer Dataset
by Sapiah Sakri and Shakila Basheer
Electronics 2023, 12(5), 1168; https://doi.org/10.3390/electronics12051168 - 28 Feb 2023
Cited by 3 | Viewed by 1759
Abstract
Accurate diagnosis of breast cancer using automated algorithms continues to be a challenge in the literature. Although researchers have conducted a great deal of work to address this issue, no definitive answer has yet been discovered. This challenge is aggravated further by the [...] Read more.
Accurate diagnosis of breast cancer using automated algorithms continues to be a challenge in the literature. Although researchers have conducted a great deal of work to address this issue, no definitive answer has yet been discovered. This challenge is aggravated further by the fact that most available datasets have imbalanced class issues, meaning that the number of cases in one class vastly outnumbers those of the others. The goal of this study was to (i) develop a reliable machine-learning-based prediction model for breast cancer based on the combination of the resampling technique and the classifier, which we called a ‘fusion model’; (ii) deal with a typical high-class imbalance problem, which is posed because the breast cancer patients’ class is significantly smaller than the healthy class; and (iii) interpret the model output to understand the decision-making mechanism. In a comparative analysis with three well-known classifiers representing classical learning, ensemble learning, and deep learning, the effectiveness of the proposed machine-learning-based approach was investigated in terms of metrics related to both generalization capability and prediction accuracy. Based on the comparative analysis, the fusion model (random oversampling techniques dataset + extreme gradient boosting classifier) affects the accuracy, precision, recall, and F1-score with the highest value of 99.9%. On the other hand, for ROC evaluation, the oversampling and hybrid sampling techniques dataset combined with extreme gradient boosting achieved 100% performance compared to the models combined with the undersampling techniques dataset. Thus, the proposed predictive model based on the fusion strategy can optimize the performance of breast cancer diagnosis classification. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence in Healthcare and Biomedical Systems)
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11 pages, 2673 KiB  
Article
Accuracy of Using a New Semi-Automated Software Package to Diagnose Osteoporotic Vertebral Fractures in Adults
by Fawaz F. Alqahtani and Paul A. Bromiley
Electronics 2023, 12(4), 847; https://doi.org/10.3390/electronics12040847 - 08 Feb 2023
Viewed by 997
Abstract
We evaluate the accuracy of a semi-automated software package for annotating landmark points on vertebral body outlines in dual-energy X-ray absorptiometry (DXA) images of adults. The aim of the study was to determine the accuracy with which a non-expert radiographer could use the [...] Read more.
We evaluate the accuracy of a semi-automated software package for annotating landmark points on vertebral body outlines in dual-energy X-ray absorptiometry (DXA) images of adults. The aim of the study was to determine the accuracy with which a non-expert radiographer could use the software to annotate vertebrae in support of osteoporotic vertebral fracture diagnosis and grading. In this study, 71 GE Lunar iDXA vertebral fracture assessment (VFA) images were used. Annotations of landmark points on vertebral body outlines were performed by four observers. Annotations consisted of 33 points on each vertebra between T4 and L4 inclusive; 11 on the upper end-plate, 8 on the anterior side, 11 on the lower end-plate, and 3 on the pedicle (429 points for each image). There were a total of 19 (26%) cases in which the non-expert radiographer made vertebral level assignment errors. All of them were one level too high (with L1 identified as T12). Their median error for landmark annotation was 1.05 mm, comparable to the 0.8 mm error achieved by the expert radiographers. Normative mean vertebral body heights vary between approximately 22 mm at T4 and 36 mm at L4 in females. Mild, moderate, and severe vertebral fragility fractures are defined through vertebral body height reductions of 20%, 25%, and 40%, respectively. Therefore, the annotation accuracy of the software when used by a non-expert was 14–23% of the height reduction indicative of a mild fracture. We conclude that, even when used by non-experts, the software can annotate vertebral body outlines accurately enough to support vertebral fragility fracture diagnosis and grading. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence in Healthcare and Biomedical Systems)
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20 pages, 65879 KiB  
Article
Segmentation of Nucleus and Cytoplasm from H&E-Stained Follicular Lymphoma
by Pranshu Saxena, Anjali Goyal, Mariyam Aysha Bivi, Sanjay Kumar Singh and Mamoon Rashid
Electronics 2023, 12(3), 651; https://doi.org/10.3390/electronics12030651 - 28 Jan 2023
Cited by 3 | Viewed by 1547
Abstract
This paper proposes a noble image segment technique to differentiate between large malignant cells called centroblasts vs. centrocytes. A new approach is introduced, which will provide additional input to an oncologist to ease the prognosis. Firstly, a H&E-stained image is projected onto L*a*b* [...] Read more.
This paper proposes a noble image segment technique to differentiate between large malignant cells called centroblasts vs. centrocytes. A new approach is introduced, which will provide additional input to an oncologist to ease the prognosis. Firstly, a H&E-stained image is projected onto L*a*b* color space to quantify the visual differences. Secondly, this transformed image is segmented with the help of k-means clustering into its three cytological components (i.e., nuclei, cytoplasm, and extracellular), followed by pre-processing techniques in the third step, where adaptive thresholding and the area filling function are applied to give them proper shape for further analysis. Finally, the demarcation process is applied to pre-processed nuclei based on the local fitting criterion function for image intensity in the neighborhood of each point. Integration of these local neighborhood centers leads us to define the global criterion of image segmentation. Unlike active contour models, this technique is independent of initialization. This paper achieved 92% sensitivity and 88.9% specificity in comparing manual vs. automated segmentation. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence in Healthcare and Biomedical Systems)
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20 pages, 2504 KiB  
Article
Next-Gen Mulsemedia: Virtual Reality Haptic Simulator’s Impact on Medical Practitioner for Higher Education Institutions
by Abhishek Kumar, Bhavana Srinivasan, Abdul Khader Jilani Saudagar, Abdullah AlTameem, Mohammed Alkhathami, Badr Alsamani, Muhammad Badruddin Khan, Zakir Hussain Ahmed, Ankit Kumar and Kamred Udham Singh
Electronics 2023, 12(2), 356; https://doi.org/10.3390/electronics12020356 - 10 Jan 2023
Cited by 10 | Viewed by 2538
Abstract
Immersive technology is one of the emerging trends in education in the twenty-first century, whether that be university training programs, or real-world technical training. However, there has been very little research into the effects and consequences of virtual reality. Various types of eLearning [...] Read more.
Immersive technology is one of the emerging trends in education in the twenty-first century, whether that be university training programs, or real-world technical training. However, there has been very little research into the effects and consequences of virtual reality. Various types of eLearning have been used to transmit information in recent years, and especially for medical education, virtual reality plays a vital role in terms of providing effective training; the virtual reality app bridged the gap between traditional learning and practical exposure. This unified reality environment enables users to simulate real-life scenarios and obtain useful information that would otherwise be unavailable. In the real world, it is difficult to grasp. In India’s education sector, virtual reality technology is also being researched at an early stage. The goal of this research paper is to assess and explain the impact of virtual reality simulators on medical students’ desire to learn. In the classroom, the core motivation hypothesis is used to boost motivation. The attention, relevance, confidence, and satisfaction (ARCS) model influenced the interpretation of virtual reality’s impact on student motivation and content update implementation. The study examined the numerous variables of virtual reality simulators and their impact on medical education, using the ARCS model as a factor analysis. According to the study, students wsould learn more and be more motivated if virtual reality simulators were used. Attention, relevance, satisfaction, and confidence indicators were used to develop motivational variables, and the results were significant. We have taken the sample of 607 students’ data for this analysis, through which we have identified the potential of VR made available to students, as well as the faculty, which has the potential to transform medical education. Instructors may be wary of incorporating new technology like VR into their curriculums, but with the support of their students’ learning habits, this may not be a problem. It may help instructors feel more confident, while also enhancing the relationship between faculty, librarians, and students. Full article
(This article belongs to the Special Issue Role of Artificial Intelligence in Healthcare and Biomedical Systems)
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