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Bioengineering
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Deep Learning for Medical Applications: Challenges and Opportunities
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Deep Learning for Medical Applications: Challenges and Opportunities

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4004

Editors

Dr. Roberta Hlavata

E-Mail Website
Guest Editor
Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia
Interests: neural network; deep learning; computer vision; image and video processing; classification human behavioral
Dr. Robert Hudec

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia
Interests: neural network; machine learning; deep learning; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Patrik Kamencay

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Information Technology, Department of Multimedia and Information-Communication Technologies, University of Zilina, 010 26 Zilina, Slovakia
Interests: digital processing; image data collection; processing and machine learning methods; neural networks (AI-enhanced 3D object recognition); image data segmentation, classification, 2D/3D recognition, and 3D reconstruction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advancements in deep learning have revolutionized the field of medical applications, offering unprecedented opportunities to enhance healthcare delivery, diagnostics, and patient outcomes. From medical imaging and genomics to personalized medicine and real-time monitoring, deep learning has demonstrated its potential to transform traditional healthcare practices. However, integrating these sophisticated algorithms into clinical settings presents significant challenges, including data scarcity, model interpretability, ethical considerations, and privacy concerns.

This Special Issue, entitled "Deep Learning for Medical Applications: Challenges and Opportunities", provides a platform for the exploration of recent developments in the application of deep learning to medical domains. By bridging the gap between innovative AI research and practical medical applications, this Special Issue aims to advance the field of medical AI while enabling the discussion of obstacles and leveraging deep learning's full potential in healthcare.

Deep learning has revolutionized the field of medical research, offering unprecedented capabilities in areas such as diagnosis, prognosis, treatment planning, and disease monitoring. This Special Issue focuses on the transformative potential of deep learning techniques in medical applications, exploring both their immense opportunities and the challenges that remain in deploying these technologies in real-world clinical settings. We welcome the submission of high-quality, original contributions that address the following topics:

  • The development and validation of deep learning models for medical image analysis (e.g., CT, MRI, X-ray, ultrasound).
  • Health and medical behavior analytics with deep learning.
  • Machine learning for image classification with CT/MRI/PET/X-ray/ultrasound.
  • The application of deep learning in bioinformatics, genomics, and personalized medicine.
  • The integration of multimodal data (e.g., imaging, electronic health records, and wearable device data) using deep learning.
  • Approaches for improving model interpretability and explainability in medical contexts.
  • Ethical and regulatory considerations in the use of AI-driven medical tools.
  • Challenges in training deep learning models with limited, imbalanced, or noisy datasets.
  • Advances in federated learning and privacy-preserving AI for healthcare applications.
  • Multimodal medical image analysis.
  • The deployment and scalability of deep learning models in clinical environments.

This Special Issue aims to present innovative research, share insights into practical implementation, and address critical gaps in the adoption of deep learning in healthcare. In this Special Issue, we welcome original research articles, reviews, and case studies that advance our understanding and application of deep learning in medical domains.

Dr. Roberta Hlavata
Dr. Robert Hudec
Dr. Patrik Kamencay
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-anonymized 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

  • deep neural networks
  • deep learning
  • machine learning
  • medical applications
  • healthcare AI
  • medical imaging
  • multimodal data integration
  • model interpretability

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Published Papers (4 papers)

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Research

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26 pages, 6421 KB  
Article
SHAP-Based Feature Augmentation and Stacking Ensemble Learning for ECG-Based Serum Potassium Abnormality Prediction
by Yi-Hsin Ko, Chuan-Sheng Hung, Chun-Hung Richard Lin, Yi-Fong Ciou, Chiang-Chi Huang, Pin-Chen Lin and Jui-Hsiu Tsai
Bioengineering 2026, 13(7), 816; https://doi.org/10.3390/bioengineering13070816 - 16 Jul 2026
Viewed by 209
Abstract
Electrocardiogram (ECG) signals contain important clinical information associated with serum potassium abnormalities. However, in Taiwan, raw patient data and original medical signals generally cannot be taken outside the hospital environment, thereby limiting their subsequent reuse and cross-institutional applications. The objective of this study [...] Read more.
Electrocardiogram (ECG) signals contain important clinical information associated with serum potassium abnormalities. However, in Taiwan, raw patient data and original medical signals generally cannot be taken outside the hospital environment, thereby limiting their subsequent reuse and cross-institutional applications. The objective of this study is to transform classification-related information contained in raw ECG signals into high-level SHAP features and to evaluate their feasibility as auxiliary or alternative features to the original wave-segment model outputs. To this end, this study proposes a time-series classification framework that integrates ECG wave-segment submodels, SHAP-based feature augmentation, and stacking ensemble learning for serum potassium abnormality prediction. Submodels are first trained separately using different ECG wave segments and their combinations. SHAP is then applied to transform the contributions of the wave-segment submodel outputs to the prediction results into high-level features. In addition, PCA features are included as a comparison baseline to analyze the effects of different feature transformation methods on classification performance. The experimental results show that incorporating SHAP-based augmented features improves ECG-based serum potassium abnormality prediction performance under most settings. Even when only SHAP-based augmented features are used for training, some models still maintain performance comparable to or better than the Baseline. Although PCA provides more stable classification balance for some patients, SHAP-based augmented features can still represent classification-related model contribution information under most settings while achieving better or comparable classification performance. Overall, even without directly using the original ECG signals in the final classification stage, these features retain a certain degree of discriminative information and demonstrate the potential to serve as alternative features to the original wave-segment model outputs. Therefore, the findings of this study provide a preliminary reference for future medical data reuse, cross-institutional collaboration, and privacy risk assessment. Full article
(This article belongs to the Special Issue Deep Learning for Medical Applications: Challenges and Opportunities)
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38 pages, 4161 KB  
Article
Mamba-Bi-LSTM with SHAP-Guided Iterative Refinement for Multimodal ARDS Diagnosis: A Dual-System Framework
by Mufeng Chen, Fuchang Luo, Jia Xie and Quansheng Ren
Bioengineering 2026, 13(7), 794; https://doi.org/10.3390/bioengineering13070794 - 10 Jul 2026
Viewed by 411
Abstract
Acute respiratory distress syndrome (ARDS) is associated with mortality rates up to 46% and remains challenging to diagnose early due to overlapping clinical presentations. We propose a dual-system framework for multimodal ARDS diagnosis that integrates a Mamba-Bi-LSTM primary discrimination system with a TreeSHAP-based [...] Read more.
Acute respiratory distress syndrome (ARDS) is associated with mortality rates up to 46% and remains challenging to diagnose early due to overlapping clinical presentations. We propose a dual-system framework for multimodal ARDS diagnosis that integrates a Mamba-Bi-LSTM primary discrimination system with a TreeSHAP-based verification system whose attribution outputs iteratively refine the primary system’s feature selection gate. The primary system processes heterogeneous clinical inputs—ventilator parameters, blood gas indices, chest imaging, and EEG signals—through a selective state-space Mamba module and bidirectional LSTM layers. The verification system applies TreeSHAP attribution to independently cross-validate primary outputs, provide clinically interpretable evidence, and supply ℓ1-normalised attribution vectors that directly modulate the Mamba feature selection gate weights during offline refinement. A confidence-and-consistency decision mechanism governs final output, and high-confidence predictions are incorporated as curriculum-filtered signals to iteratively recalibrate both systems through a confidence-gated offline refinement protocol. Evaluated on 3742 held-out patients from MIMIC-IV (internal test) and 2594 patients from the eICU Collaborative Research Database across 208 US hospitals (external validation), the complete system achieves 92.8% accuracy and an F1 score of 0.889 after offline iterative recalibration on the internal test set, with 91.6% accuracy and F1 of 0.871 on external validation, extending early warning time from 5.2 to 9.7 h. The P/F ratio consistently ranks as the top predictive feature in alignment with the Berlin definition. Ablation experiments confirm that EEG integration independently contributes a 2.7 percentage point accuracy gain and a 1.9-h extension of the warning window (McNemar χ2=27.0, p<0.001). All performance improvements over single-modality baselines and over existing methods are statistically significant (p<0.001, Bonferroni-corrected). End-to-end processing latency of 350 ms per case is compatible with real-time ICU deployment. Full article
(This article belongs to the Special Issue Deep Learning for Medical Applications: Challenges and Opportunities)
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32 pages, 6103 KB  
Article
An Optimal Deep Hybrid Framework with Selective Kernel U-Net for Skin Lesion Detection and Classification
by Guzal Gulmirzaeva, Robert Hudec, Baxtiyorjon Akbaraliev and Batirbek Samandarov
Bioengineering 2026, 13(4), 427; https://doi.org/10.3390/bioengineering13040427 - 6 Apr 2026
Viewed by 1190
Abstract
Early and accurate detection of skin cancer is critical for reducing mortality rates, particularly for malignant melanoma. Automated analysis of dermoscopic images has gained significant attention due to its potential to support clinical diagnosis and overcome the limitations of manual inspection. Motivated by [...] Read more.
Early and accurate detection of skin cancer is critical for reducing mortality rates, particularly for malignant melanoma. Automated analysis of dermoscopic images has gained significant attention due to its potential to support clinical diagnosis and overcome the limitations of manual inspection. Motivated by challenges such as image noise, low contrast, lesion variability, and redundant feature representation, this study proposes an optimal deep hybrid framework for skin lesion detection and classification. The objective of this work is to design a robust and efficient system that integrates advanced preprocessing, precise segmentation, optimal feature selection, and accurate classification. Initially, contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE) and noise reduction using Wiener filtering are applied to improve image quality. Lesion regions are then segmented using a Selective Kernel U-Net (SK-UNet), which adaptively captures multi-scale spatial information. Subsequently, discriminative color, texture, and shape features are extracted and optimized using the Fossa Optimization Algorithm (FOA) to eliminate redundancy. A hybrid one-dimensional Convolutional Neural Network–Gated Recurrent Unit (1D-CNN–GRU) classifier is employed for final classification, learning both spatial and sequential feature patterns. Experimental evaluation on the ISIC and DermMNIST datasets demonstrates that the proposed framework achieves classification accuracies of 97.6% and 95.6%, respectively, outperforming several existing methods. The results confirm that the proposed hybrid framework provides reliable, accurate, and scalable skin cancer diagnosis, highlighting its potential for assisting clinical decision-making and early detection. Full article
(This article belongs to the Special Issue Deep Learning for Medical Applications: Challenges and Opportunities)
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Review

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38 pages, 730 KB  
Review
Artificial Intelligence Applications in Implant Positioning, Dislocation Risk Prediction, and Surgical Indications in Orthopaedic Surgery
by Mihai Emanuel Gherghe, Alex-Gabriel Grigore, Iosif-Aliodor Timofticiuc, Adelina-Elena Moise, Constantin-Adrian Andrei, Serban Dragosloveanu, Dana-Georgiana Nedelea, Łukasz Pulik, Catalin Anghel, Cristian Scheau and Romica Cergan
Bioengineering 2026, 13(6), 610; https://doi.org/10.3390/bioengineering13060610 - 23 May 2026
Viewed by 687
Abstract
Background: Artificial intelligence (AI) is becoming increasingly integrated into orthopaedic surgery for tasks such as implant positioning, dislocation risk prediction, and surgical decision-making. However, the current evidence varies widely across anatomical regions and applications. Methods: A structured narrative review was conducted using PubMed [...] Read more.
Background: Artificial intelligence (AI) is becoming increasingly integrated into orthopaedic surgery for tasks such as implant positioning, dislocation risk prediction, and surgical decision-making. However, the current evidence varies widely across anatomical regions and applications. Methods: A structured narrative review was conducted using PubMed and Web of Science Core Collection to identify studies applying machine learning or deep learning in orthopaedic procedures, focusing on parameters such as the anatomical region addressed, data types used, primary AI tasks, evaluation designs, and validation strategies. Reviews and meta-analyses were excluded. Study selection was summarized using a PRISMA-style flow diagram, and included studies were narratively synthesized according to anatomical region, AI task, imaging modality, validation strategy, and clinical relevance. Results: We identified three main application areas: (1) AI in imaging-driven planning and implant positioning, often linked with navigation or robotic systems; (2) postoperative evaluation related to implants; and (3) prediction of clinically relevant outcomes such as dislocation risk. The strongest evidence is found in hip arthroplasty, where AI improves measurement accuracy and workflow efficiency, whereas applications in knee, shoulder, and spine surgery are less developed and often supported by smaller studies. Although existing risk prediction models demonstrate good performance, their generalizability is hindered by limited external validation and inconsistent reporting. Conclusions: Overall, while AI shows significant promise in enhancing various aspects of orthopaedic surgery, stronger links between technical advancements and patient outcomes are needed. Future research should prioritize extensive validations, workflow-aware evaluations, failure analysis, and adherence to AI-specific reporting guidelines to facilitate safe and effective clinical implementation. Full article
(This article belongs to the Special Issue Deep Learning for Medical Applications: Challenges and Opportunities)
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