Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
3.7
Journals
Bioengineering
Special Issues
Machine Learning and Deep Learning Applications in Healthcare, 2nd Edition
share announcement

Machine Learning and Deep Learning Applications in Healthcare, 2nd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 6120

Special Issue Editors

Dr. Jorge Mateo

E-Mail Website
Guest Editor
Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
Interests: artificial intelligence; machine learning; deep learning; medical decision support systems; biomedical diagnostic techniques; personalized medical treatments; molecular sciences; medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Ana María Torres Aranda

E-Mail Website
Guest Editor
1. Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
2. Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
Interests: machine learning; biomedical signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning is one of the research topics that attracts a lot of attention from healthcare system researchers. Compared to traditional machine learning methods, deep learning algorithms demonstrate their ability to train models from large datasets and images.

The computational capacity of deep learning models has enabled fast, accurate, and efficient operations in healthcare. Deep learning networks are transforming patient care and play a fundamental role in healthcare systems in clinical practice. Computer vision, natural language processing, and reinforcement learning are the most used deep learning techniques in healthcare. Moreover, these algorithms have significantly outperformed the performance of traditional methodologies for computer vision, natural language processing, robotics, and other fields.

This Special Issue on “Machine Learning and Deep Learning Applications in Healthcare, 2nd Edition” will focus on research works on the latest applications of deep learning in data analysis in different areas of healthcare research. The topics of interest for this Special Issue include, but are not limited to, the following:

  • Healthcare data analytics;
  • Medical imaging;
  • Deep learning in time-series processing;
  • Biomedical diagnostic techniques;
  • Personalized medical treatments;
  • Predictive modelling for improving healthcare;
  • Medical decision support systems;
  • Multimodal data processing and analysis for smart health;
  • Medical systems based on big data and artificial intelligence;
  • Artificial intelligence;
  • Other related topics regarding healthcare, deep learning, and biomedical engineering.

Dr. Jorge Mateo
Dr. Ana María Torres Aranda
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 250 words) can be sent to the Editorial Office for assessment.

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

  • machine learning
  • deep learning
  • big-data enabled healthcare systems
  • biomedical diagnostic techniques
  • diagnostic imaging
  • medical decision making
  • digital pathology
  • digital radiology
  • artificial neural networks
  • artificial intelligence
  • smart health

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 3789 KB  
Article
Unveiling the Digital Phenotype of Physical Activity Behavior in Community-Dwelling Older Adults Using Machine Learning
by Anas Abdulghani, Kim Daniels and Bruno Bonnechère
Bioengineering 2026, 13(2), 205; https://doi.org/10.3390/bioengineering13020205 - 11 Feb 2026
Viewed by 729
Abstract
Physical activity (PA) is an important factor for maintaining health and well-being, especially in older adults. This study aims to apply machine learning methods to predict PA patterns and identify key factors influencing these behaviors among community-dwelling older adults. Linear and Logistic Regression, [...] Read more.
Physical activity (PA) is an important factor for maintaining health and well-being, especially in older adults. This study aims to apply machine learning methods to predict PA patterns and identify key factors influencing these behaviors among community-dwelling older adults. Linear and Logistic Regression, Elastic Net, and Light Gradient Boosting Machine (LightGBM) models were used to analyze cross-sectional data. While longitudinal data collected over 14 days were analyzed using LightGBM, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). The most important predictors identified in the cross-sectional analysis were the Exercise Self-efficacy Scale (ESES) for PA levels and the Geriatric Depression Scale (GDS) for the International Physical Activity Questionnaire (IPAQ) as a continuous measurement. In the longitudinal analysis, using a seven-day sequence of step count data provided the best performance for forecasting physical activity for the entire next day. Overall, the findings indicate that combining wearable sensor data with machine learning and deep learning methods can provide valuable insights into physical activity behaviors among older adults. In the cross-sectional analysis, psychological and motivational factors such as self-efficacy were identified as important factors for activity levels, while in the longitudinal analysis, using a week of past step count data provided the most reliable predictions of future-day physical activity. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications in Healthcare, 2nd Edition)
►▼ Show Figures

Figure 1

17 pages, 2930 KB  
Article
Beyond VI-RADS Uncertainty: Leveraging Spatiotemporal DCE-MRI to Predict Bladder Cancer Muscle Invasion
by Minghui Song, Haonan Ren, Lijuan Wang, Yihang Zhou, Xing Tang, Huanjun Wang, Yan Guo, Yang Liu, Hongbing Lu and Xiaopan Xu
Bioengineering 2025, 12(12), 1338; https://doi.org/10.3390/bioengineering12121338 - 8 Dec 2025
Viewed by 714
Abstract
Background: The Vesical Imaging-Reporting and Data System (VI-RADS) has limited diagnostic accuracy in distinguishing non-muscle-invasive bladder cancer (NMIBC) within VI-RADS categories 2 and 3, despite its value for overall NMIBC assessment. Dynamic contrast-enhanced MRI (DCE-MRI), which reflects tumor vascularity, holds promise for [...] Read more.
Background: The Vesical Imaging-Reporting and Data System (VI-RADS) has limited diagnostic accuracy in distinguishing non-muscle-invasive bladder cancer (NMIBC) within VI-RADS categories 2 and 3, despite its value for overall NMIBC assessment. Dynamic contrast-enhanced MRI (DCE-MRI), which reflects tumor vascularity, holds promise for improving these challenging cases but remains underutilized due to unexploited spatiotemporal information. Methods: We developed a deep learning model to comprehensively quantify spatiotemporal features from multiphase DCE-MRI in 184 patients with VI-RADS 2 or 3 (training: n = 115, validation: n = 20, testing: n = 49). The model integrated multiscale feature extraction and contextual attention mechanisms to enhance diagnostic performance. Results: The model outperformed established benchmarks (e.g., VGG, ResNet) and the conventional VI-RADS ≤ 2 threshold (sensitivity: 0.67 for NMIBC), achieving a sensitivity of 0.90 (95% CI: 0.81–0.96) for NMIBC and an area under the curve (AUC) of 0.82 (95% CI: 0.75–0.89) for overall classification. Visualizations confirmed its ability to identify key spatiotemporal patterns linked to muscle invasion. Conclusions: By leveraging comprehensive spatiotemporal information from DCE-MRI, our deep learning model significantly improves NMIBC diagnosis in VI-RADS 2/3 cases, offering a clinically valuable tool to address the limitations of current VI-RADS assessment. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications in Healthcare, 2nd Edition)
►▼ Show Figures

Figure 1

28 pages, 3650 KB  
Article
Gastrointestinal Lesion Detection Using Ensemble Deep Learning Through Global Contextual Information
by Vikrant Aadiwal, Vishesh Tanwar, Bhisham Sharma and Dhirendra Prasad Yadav
Bioengineering 2025, 12(12), 1329; https://doi.org/10.3390/bioengineering12121329 - 5 Dec 2025
Cited by 1 | Viewed by 1182
Abstract
The presence of subtle mucosal abnormalities makes small bowel Crohn’s disease (SBCD) and other gastrointestinal lesions difficult to detect, as these features are often very subtle and can closely resemble other disorders. Although the Kvasir and Esophageal Endoscopy datasets offer high-quality visual representations [...] Read more.
The presence of subtle mucosal abnormalities makes small bowel Crohn’s disease (SBCD) and other gastrointestinal lesions difficult to detect, as these features are often very subtle and can closely resemble other disorders. Although the Kvasir and Esophageal Endoscopy datasets offer high-quality visual representations of various parts of the GI tract, their manual interpretation and analysis by clinicians remain labor-intensive, time-consuming, and prone to subjective variability. To address this, we propose a generalizable ensemble deep learning framework for gastrointestinal lesion detection, capable of identifying pathological patterns such as ulcers, polyps, and esophagitis that visually resemble SBCD-associated abnormalities. Further, the classical convolutional neural network (CNN) extracts shallow high-dimensional features; due to this, it may miss the edges and complex patterns of the gastrointestinal lesions. To mitigate these limitations, this study introduces a deep learning ensemble framework that combines the strengths of EfficientNetB5, MobileNetV2, and multi-head self-attention (MHSA). EfficientNetB5 extracts detailed hierarchical features that help distinguish fine-grained mucosal structures, while MobileNetV2 enhances spatial representation with low computational overhead. The MHSA module further improves the model’s global correlation of the spatial features. We evaluated the model on two publicly available DBE datasets and compared the results with four state-of-the-art methods. Our model achieved classification accuracies of 99.25% and 98.86% on the Kvasir and Kaither datasets. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications in Healthcare, 2nd Edition)
►▼ Show Figures

Figure 1

12 pages, 1417 KB  
Article
Classification of Osteonecrosis of the Femoral Head Stage on Radiographic Images Using Deep Learning Techniques
by Hyun Hee Lee, Joeun Jeong, Taehoon Shin and Dong-Sik Chae
Bioengineering 2025, 12(12), 1319; https://doi.org/10.3390/bioengineering12121319 - 3 Dec 2025
Cited by 1 | Viewed by 1297
Abstract
While magnetic resonance imaging (MRI) is effective for detecting early-stage osteonecrosis of the femoral head (ONFH), it is often expensive and less accessible; conversely, radiography is more widely accessible but has limited sensitivity for early-stage diagnosis. We developed a deep learning approach using [...] Read more.
While magnetic resonance imaging (MRI) is effective for detecting early-stage osteonecrosis of the femoral head (ONFH), it is often expensive and less accessible; conversely, radiography is more widely accessible but has limited sensitivity for early-stage diagnosis. We developed a deep learning approach using radiographic images to effectively classify ONFH stages, providing a more accessible method for early diagnosis and disease stage differentiation. The dataset consisted of 909 hip radiographs, yielding 1818 femoral head images (grade 0:1495; grade 1:80; grade 2:114; grade 3:93; grade 4:36). A U-Net model was used to segment the femoral heads, achieving a Dice similarity coefficient (DSC) of 0.977 on the test set, allowing precise localization of the region of interest. A variational autoencoder (VAE) was then trained using 1270 grade-0 images for training and 112 for validation to construct a normative latent distribution representing healthy femoral heads. When ONFH data from all grades were projected into the latent space, significant differences in Mahalanobis distance distributions were observed across most ONFH stages. No significant difference was found between grades 0 and 1 (p = 0.06), consistent with known radiographic subtlety. However, grades 2–4 showed significant deviation from grade 0, and significant differences were also observed among mid- and late-stage grades. These findings demonstrate that the proposed method effectively separates healthy and diseased femoral heads and captures gradewise structural progression within the latent space. This radiograph-based normative modeling approach offers an accessible alternative to MRI for ONFH stage differentiation, particularly in resource-limited clinical environments. Although early-stage differentiation remains challenging, the results highlight the potential of radiograph-based deep learning systems to improve diagnostic efficiency and support future automated ONFH staging workflows. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications in Healthcare, 2nd Edition)
►▼ Show Figures

Figure 1

28 pages, 1342 KB  
Article
Cognitively Inspired Federated Learning Framework for Interpretable and Privacy-Secured EEG Biomarker Prediction of Depression Relapse
by Sana Yasin, Umar Draz, Tariq Ali, Mohammad Hijji, Muhammad Ayaz, El-Hadi M. Aggoune and Isha Yasin
Bioengineering 2025, 12(10), 1032; https://doi.org/10.3390/bioengineering12101032 - 26 Sep 2025
Cited by 1 | Viewed by 1497
Abstract
Depression relapse is a common issue during long-term care. We introduce a privacy-aware explainable personalized federated learning (PFL) framework that incorporates layer-wise relevance propagation and Shapley value analysis to provide patient-specific interpretable predictions from EEG. The study is conducted with the publicly available [...] Read more.
Depression relapse is a common issue during long-term care. We introduce a privacy-aware explainable personalized federated learning (PFL) framework that incorporates layer-wise relevance propagation and Shapley value analysis to provide patient-specific interpretable predictions from EEG. The study is conducted with the publicly available Healthy Brain Network (HBN) dataset, with analysis conducted for n = 100 subjects with resting-state 128-channel EEG with accompanying psychometric scores, and subject-wise 10-fold cross-validation is used to assess the performance of the model. Multi-channel EEG features and standardized symptom scales are jointly modeled to both increase the clinical context of the model and avoid leakage issues. This results in overall accuracy, precision, recall, and F1-score values of 92%, 91%, 93%, and 90.5%, respectively. The attribution maps from the model suggest region-anchored spectral patterns that are associated with relapse risk, providing clinical interpretability, and the federated setup of the model allows for a privacy-aware training setup that is more easily adaptable to multi-site deployment. Together, these results suggest a scalable and clinically feasible approach to trustworthy relapse monitoring with earlier intervention. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applications in Healthcare, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop