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Bioengineering
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Artificial Intelligence in Biomedical Imaging, Biosignals and Healthcare
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Artificial Intelligence in Biomedical Imaging, Biosignals and Healthcare

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 557

Special Issue Editors

Dr. Ioannis Kakkos

E-Mail Website
Guest Editor
Department of Electrical and Computing Engineering, National Technical University of Athens, 15780 Athens, Greece
Interests: transmission of nerve stimuli; the study of cognitive systems and processes; the development of AI techniques for image and signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George K. Matsopoulos

E-Mail Website
Guest Editor
Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Zografos, Greece
Interests: medical imaging; biomedical signal processing; medical decision support systems; EMhealth; biosensors; virtual reality applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of advanced biomedical data processing and artificial intelligence (AI) into biomedical imaging, biosignals, and healthcare is revolutionizing the field, enabling more precise diagnostics, personalized treatments, and improved patient outcomes. Recent advancements in imaging technologies, AI-driven image analyses, and computational modeling are transforming how we understand and treat various medical conditions.

This Special Issue on “Artificial Intelligence in Biomedical Imaging, Biosignals and Healthcare” will focus on original research papers and comprehensive reviews that explore state-of-the-art methodologies and innovations in this rapidly evolving field. The topics of interest for this Special Issue include, but are not limited to, the following:

  • Advanced techniques for the processing and analysis of biomedical data;
  • AI-driven algorithms for the segmentation and interpretation of medical images;
  • Machine learning models for predicting patient-specific disease progression and treatment outcomes;
  • The integration of AI with medical imaging modalities, such as EEG, ECG, MRI, CT, and ultrasound;
  • The development and application of AI in personalized diagnostics and therapy planning;
  • The quantitative analysis of biomedical images for tissue characterization and disease detection;
  • AI in the analysis of high-dimensional biomedical data and omics data integration;
  • Verification, validation, and uncertainty quantification in AI-based medical imaging;
  • Advanced computational models informed by molecular and cellular data for predicting tissue behavior;
  • Real-time AI applications in clinical settings, including surgery and radiology.

We invite researchers to submit work that pushes the boundaries of biomedical data processing and AI in healthcare, contributing to the advancement of this crucial field. All research areas relevant to the application of advanced computational techniques and AI in biomedical imaging, biosignals, and healthcare are welcome.

Sincerely,

Dr. Ioannis Kakkos
Prof. Dr. George K. Matsopoulos
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

  • biomedical data processing
  • artificial intelligence in healthcare
  • medical image segmentation
  • predictive modeling in medicine
  • machine learning in diagnostics
  • personalized treatment planning
  • quantitative image analysis
  • AI-driven medical imaging
  • computational modeling in healthcare
  • real-time AI applications in medicine

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

Published Papers (2 papers)

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18 pages, 4305 KiB  
Article
Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods
by Qi Sang, Chen Chen and Zeguo Shao
Bioengineering 2025, 12(5), 449; https://doi.org/10.3390/bioengineering12050449 - 24 Apr 2025
Viewed by 124
Abstract
Depression has become one of the most common mental illnesses, causing severe physical and mental harm. To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD [...] Read more.
Depression has become one of the most common mental illnesses, causing severe physical and mental harm. To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning algorithms with a hybrid machine learning model based on a stacking ensemble technique. The hybrid model employed K-nearest neighbors (KNN), decision tree (DT), and Extreme Gradient Boosting (XGBoost) as base learners and used a DT as the meta-learner. Compared with traditional single methods, the hybrid approach significantly improved detection accuracy by leveraging the strengths of different algorithms. In addition, this study divided the brain regions into the left and right temporal lobes and extracted both linear and nonlinear features to comprehensively capture the complexity and dynamic behavior of EEG signals, enhancing the model’s ability to distinguish features across different brain regions. The experimental results showed that among the eight traditional machine learning methods, the KNN classifier achieved the highest detection accuracy of 96.97% in the left temporal lobe region. In contrast, the stacking hybrid learning model further increased the detection accuracy to 98.07%, significantly outperforming the single models. Moreover, the analysis of the brain region segmentation revealed that the left temporal lobe exhibited higher discriminative power in detecting MDD, highlighting its important role in the neurobiology of depression. This study provides a solid foundation for developing more efficient and portable methods for detecting depression, offering new perspectives and approaches for EEG-based MDD detection, and contributing to the improvement in objectivity and precision in depression diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging, Biosignals and Healthcare)
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15 pages, 7166 KiB  
Article
Comparative Analysis of Deep Neural Networks for Automated Ulcerative Colitis Severity Assessment
by Andreas Vezakis, Ioannis Vezakis, Ourania Petropoulou, Stavros T. Miloulis, Athanasios Anastasiou, Ioannis Kakkos and George K. Matsopoulos
Bioengineering 2025, 12(4), 413; https://doi.org/10.3390/bioengineering12040413 - 13 Apr 2025
Viewed by 246
Abstract
Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by continuous inflammation of the colon and rectum. Accurate disease assessment is essential for effective treatment, with endoscopic evaluation, particularly the Mayo Endoscopic Score (MES), serving as a key diagnostic tool. However, [...] Read more.
Background: Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by continuous inflammation of the colon and rectum. Accurate disease assessment is essential for effective treatment, with endoscopic evaluation, particularly the Mayo Endoscopic Score (MES), serving as a key diagnostic tool. However, MES measurement can be subjective and inconsistent, leading to variability in treatment decisions. Deep learning approaches have shown promise in providing more objective and standardized assessments of UC severity. Methods: This study utilized publicly available endoscopic images of UC patients to analyze and compare the performance of state-of-the-art deep neural networks for automated MES classification. Several state-of-the-art architectures were tested to determine the most effective model for grading disease severity. The F1 score, accuracy, recall, and precision were calculated for all models, and statistical analysis was conducted to verify statistically significant differences between the networks. Results: VGG19 was found to be the best-performing network, achieving a QWK score of 0.876 and a macro-averaged F1 score of 0.7528 across all classes. However, the performance differences among the top-performing models were very small suggesting that selection should depend on specific deployment requirements. Conclusions: This study demonstrates that multiple state-of-the-art deep neural network architectures could automate UC severity classification. Simpler architectures were found to achieve competitive results with larger models, challenging the assumption that larger networks necessarily provide better clinical outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Imaging, Biosignals and Healthcare)
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