AI in Biomedical Image Segmentation, Processing and Analysis

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 813

Special Issue Editors


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Guest Editor
Artificial Intelligence and Cyber Futures Institute, Charles University, Bathurst, NSW 2795, Australia
Interests: artificial intelligence; machine learning; deep learning; computer vision; biomedical imaging; health informatics; bioinformatics; explainable AI; automated decision-making systems

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Guest Editor
School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, Australia
Interests: artificial intelligence; image processing; medical imaging; computer vision; machine learning; sensor networks; IoT; edge computing

E-Mail Website
Guest Editor
Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW, Australia
Interests: artificial intelligence; machine learning; deep learning; computer vision; digital health data science; health informatics; bioinformatics; medical image processing (MRI, fMRI, Ultrasound, X-ray); neuroimaging; EEG; ECG analysis; explainable AI and transparent decision-making systems
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Special Issue Information

Dear Colleagues,

In recent years, Artificial Intelligence (AI) has profoundly transformed biomedical image analysis, with segmentation emerging as one of the most critical and impactful tasks. These advancements have accelerated the integration of AI-driven methods into clinical workflows, enabling improved diagnostic accuracy, early disease detection, treatment planning, and image-guided interventions.

Recent developments have led to the deployment of AI-based medical devices in real-world healthcare settings, marking a major milestone in the transition from research to practice. Nevertheless, significant challenges remain in ensuring robustness, interpretability, scalability, and ethical deployment of these systems across diverse medical contexts.

This Special Issue aims to gather high-quality contributions that explore innovative approaches, methodologies, and applications of AI in biomedical image segmentation, processing, and analysis. We welcome both original research articles and comprehensive reviews that highlight breakthroughs and future directions in this fast-evolving field.

Topics of interest include, but are not limited to, the following:

  • Medical image segmentation using deep learning and hybrid methods;
  • AI-based medical image registration and reconstruction;
  • Medical image classification and recognition;
  • Patient/treatment stratification with imaging biomarkers;
  • Synthetic medical image generation and augmentation;
  • Image-guided surgery and radiotherapy assisted by AI;
  • Radiomics and predictive modelling;
  • Explainable and trustworthy AI in medicine.

We look forward to your valuable contributions to advance knowledge in this exciting and impactful domain.

Dr. Toufique Ahmed Soomro
Prof. Dr. Lihong Zheng
Dr. Mohammad Ali Moni
Guest Editors

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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

  • artificial intelligence
  • biomedical imaging
  • image segmentation
  • deep learning
  • medical image processing
  • radiomics
  • explainable AI
  • computer-aided diagnosis

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Published Papers (1 paper)

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Research

34 pages, 4356 KB  
Article
Neural Efficiency and Attentional Instability in Gaming Disorder: A Task-Based Occipital EEG and Machine Learning Study
by Riaz Muhammad, Ezekiel Edward Nettey-Oppong, Muhammad Usman, Saeed Ahmed Khan Abro, Toufique Ahmed Soomro and Ahmed Ali
Bioengineering 2026, 13(2), 152; https://doi.org/10.3390/bioengineering13020152 - 28 Jan 2026
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Abstract
Gaming Disorder (GD) is becoming more widely acknowledged as a behavioral addiction characterized by impaired control and functional impairment. While resting-state impairments are well understood, the neurophysiological dynamics during active gameplay remain underexplored. This study identified task-based occipital EEG biomarkers of GD and [...] Read more.
Gaming Disorder (GD) is becoming more widely acknowledged as a behavioral addiction characterized by impaired control and functional impairment. While resting-state impairments are well understood, the neurophysiological dynamics during active gameplay remain underexplored. This study identified task-based occipital EEG biomarkers of GD and assessed their diagnostic utility. Occipital EEG (O1/O2) data from 30 participants (15 with GD, 15 controls) collected during active mobile gaming were used in this study. Spectral, temporal, and nonlinear complexity features were extracted. Feature relevance was ranked using Random Forest, and classification performance was evaluated using Leave-One-Subject-Out (LOSO) cross-validation to ensure subject-independent generalization across five models (Random Forest, KNN, SVM, Decision Tree, ANN). The GD group exhibited paradoxical “spectral slowing” during gameplay, characterized by increased Delta/Theta power and decreased Beta activity relative to controls. Beta variability was identified as a key biomarker, reflecting altered attentional stability, while elevated Alpha power suggested potential neural habituation or sensory gating. The Decision Tree classifier emerged as the most robust model, achieving a classification accuracy of 80.0%. Results suggest distinct neurophysiological patterns in GD, where increased low-frequency power may reflect automatized processing or “Neural Efficiency” despite active task engagement. These findings highlight the potential of occipital biomarkers as accessible and objective screening metrics for Gaming Disorder. Full article
(This article belongs to the Special Issue AI in Biomedical Image Segmentation, Processing and Analysis)
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