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Bioengineering
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Artificial Intelligence in Healthcare
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Artificial Intelligence in Healthcare

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 7038

Special Issue Editors

Prof. Dr. Jinshan Tang

E-Mail Website
Guest Editor
Department of Health Administration and Policy, George Mason University, Fairfax, VA 22030, USA
Interests: AI in medicine; medical image analysis; medical imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sos Agaian

E-Mail Website
Guest Editor
Computer Science Department, City University of New York, New York, NY 10314, USA
Interests: AI in medicine; biomedical data mining; object recognition; signal processing; computer-aided food quality inspection; 3D imaging visible and thermal sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI has advanced significantly in the past ten years, offering great potential to revolutionize the healthcare system. AI can be applied to various aspects of healthcare, such as disease detection and diagnosis, prognosis prediction, pre-treatment analysis, post-treatment evaluation, pandemic monitoring, and more.

However, AI still faces many challenges in medical applications and requires further development to meet the demands of real health scenarios. One of these challenges is that the performance of healthcare AI systems often needs to meet real-world needs. Therefore, enhancing the performance of healthcare AI systems is a crucial issue. In order to address this issue, conducting in-depth research on key technologies within the various subsystems employed in healthcare AI systems is essential and necessary.

This Special Issue aims to attract high-quality papers on AI in healthcare and address the complex challenges that arise within the context of healthcare applications. The Special Issue's focus spans various fields, such as machine learning, medical image processing, pattern recognition, and others. Topics of interest include, but are not limited to, the following:

  • Intelligent medical systems
  • Machine learning for medical applications
  • AI for medical image analysis
  • AI-powered patient monitoring systems.
  • AI for prognosis prediction
  • AI for patient care safety, quality, and research
  • AI for pandemic monitoring
  • AI for disease detection and Diagnosis

Prof. Dr. Jinshan Tang
Prof. Dr. Sos Agaian
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • AI
  • healthcare
  • disease diagnosis
  • disease detection
  • machine learning

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Published Papers (4 papers)

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Research

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25 pages, 4229 KiB  
Article
Convolutional Neural Network Incorporating Multiple Attention Mechanisms for MRI Classification of Lumbar Spinal Stenosis
by Juncai Lin, Honglai Zhang and Hongcai Shang
Bioengineering 2024, 11(10), 1021; https://doi.org/10.3390/bioengineering11101021 - 13 Oct 2024
Viewed by 560
Abstract
Background: Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis is critical for effective treatment. However, manual diagnosis using MRI images is time consuming and subjective, leading to a need for automated methods. [...] Read more.
Background: Lumbar spinal stenosis (LSS) is a common cause of low back pain, especially in the elderly, and accurate diagnosis is critical for effective treatment. However, manual diagnosis using MRI images is time consuming and subjective, leading to a need for automated methods. Objective: This study aims to develop a convolutional neural network (CNN)-based deep learning model integrated with multiple attention mechanisms to improve the accuracy and robustness of LSS classification via MRI images. Methods: The proposed model is trained on a standardized MRI dataset sourced from multiple institutions, encompassing various lumbar degenerative conditions. During preprocessing, techniques such as image normalization and data augmentation are employed to enhance the model’s performance. The network incorporates a Multi-Headed Self-Attention Module, a Slot Attention Module, and a Channel and Spatial Attention Module, each contributing to better feature extraction and classification. Results: The model achieved 95.2% classification accuracy, 94.7% precision, 94.3% recall, and 94.5% F1 score on the validation set. Ablation experiments confirmed the significant impact of the attention mechanisms in improving the model’s classification capabilities. Conclusion: The integration of multiple attention mechanisms enhances the model’s ability to accurately classify LSS in MRI images, demonstrating its potential as a tool for automated diagnosis. This study paves the way for future research in applying attention mechanisms to the automated diagnosis of lumbar spinal stenosis and other complex spinal conditions. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare)
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21 pages, 7854 KiB  
Article
An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction
by Richard Wijaya, Faisal Saeed, Parnia Samimi, Abdullah M. Albarrak and Sultan Noman Qasem
Bioengineering 2024, 11(7), 672; https://doi.org/10.3390/bioengineering11070672 - 2 Jul 2024
Viewed by 1409
Abstract
Stroke poses a significant health threat, affecting millions annually. Early and precise prediction is crucial to providing effective preventive healthcare interventions. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. By employing the cross-industry [...] Read more.
Stroke poses a significant health threat, affecting millions annually. Early and precise prediction is crucial to providing effective preventive healthcare interventions. This study applied an ensemble machine learning and data mining approach to enhance the effectiveness of stroke prediction. By employing the cross-industry standard process for data mining (CRISP-DM) methodology, various techniques, including random forest, ExtraTrees, XGBoost, artificial neural network (ANN), and genetic algorithm with ANN (GANN) were applied on two benchmark datasets to predict stroke based on several parameters, such as gender, age, various diseases, smoking status, BMI, HighCol, physical activity, hypertension, heart disease, lifestyle, and others. Due to dataset imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied to the datasets. Hyperparameter tuning optimized the models via grid search and randomized search cross-validation. The evaluation metrics included accuracy, precision, recall, F1-score, and area under the curve (AUC). The experimental results show that the ensemble ExtraTrees classifier achieved the highest accuracy (98.24%) and AUC (98.24%). Random forest also performed well, achieving 98.03% in both accuracy and AUC. Comparisons with state-of-the-art stroke prediction methods revealed that the proposed approach demonstrates superior performance, indicating its potential as a promising method for stroke prediction and offering substantial benefits to healthcare. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare)
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13 pages, 1985 KiB  
Article
Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach
by Khoula Al Sadi and Wamadeva Balachandran
Bioengineering 2024, 11(4), 379; https://doi.org/10.3390/bioengineering11040379 - 15 Apr 2024
Cited by 1 | Viewed by 1351
Abstract
This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of ‘Artificial Intelligence in Healthcare’. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of [...] Read more.
This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of ‘Artificial Intelligence in Healthcare’. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman’s population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model’s performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model’s capability in diabetes detection. The implementation of this LSTM model in Oman’s healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges relating to globally diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare)
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Review

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14 pages, 293 KiB  
Review
Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review
by Seyed Mohammadrasoul Naeimi, Shayan Darvish, Bahareh Nazemi Salman and Ionut Luchian
Bioengineering 2024, 11(5), 431; https://doi.org/10.3390/bioengineering11050431 - 27 Apr 2024
Viewed by 2638
Abstract
Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially [...] Read more.
Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Healthcare)
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