Journal Special Issues

BMC Medical Imaging

Artificial Intelligence and Quantitative Imaging Biomarkers for Precision Medicine

Advances in medical imaging technologies such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound have enabled the acquisition of large and high-dimensional imaging data. Extracting meaningful quantitative information from these datasets remains a major challenge but also presents significant opportunities for improving disease diagnosis, prognosis, and treatment planning.

 

Artificial intelligence (AI) techniques, including machine learning, deep learning, and radiomics, have emerged as powerful tools for analyzing medical imaging data and extracting quantitative imaging biomarkers. These biomarkers can capture structural, functional, and textural patterns that may not be visible through conventional visual assessment, enabling improved disease characterization and supporting precision medicine approaches.

 

This Special Issue aims to highlight recent advances in AI-driven quantitative analysis of medical images and the development of imaging biomarkers for clinical decision support. We particularly welcome contributions that integrate advanced computational methods with clinical applications, including studies involving multimodal imaging data and research demonstrating the clinical utility, interpretability, and robustness of AI-based imaging biomarkers.

Topics of Interest

(Potential topics include but are not limited to)

AI for Medical Image Analysis

  • Deep learning for medical image segmentation and classification
  • Machine learning for multimodal imaging data
  • Visual Question Answering (VQA) systems for interactive medical image interpretation and clinical decision support

Quantitative Imaging Biomarkers

  • Radiomics and radiogenomics
  • Biomarker discovery from imaging data
  • Imaging biomarkers for disease prognosis

Precision Medicine Applications

  • Imaging-based disease progression prediction
  • Patient-specific treatment planning
  • Personalized diagnostic tools

Multimodal Imaging Integration

  • Fusion of MRI, CT, PET, and ultrasound data
  • Cross-modality feature extraction
  • Multi-source imaging analysis

Clinical Translation and Trustworthy AI

  • Explainable AI in medical imaging
  • Uncertainty estimation in imaging models
  • Clinical validation of AI-based imaging biomarkers

Guest Editors

  • Prof. Efthyvoulos Kyriacou, Cyprus University of Technology, Cyprus
    Email: efthyvoulos.kyriacou[a]cut.ac.cy
  • Andreas Panayides, CYENS Centre of Excellence, Cyprus
    Email: a.panayides[a]cyens.org.cy
  • Charis Styliadis, Lab of Medical Physics and Digital Innovation,
    Aristotle University of Thessaloniki, Greece
    Email: styliadis[a]hotmail.com
  • Marios Pattichis, University of New Mexico, USA
    Email: pattichi[a]unm.edu

Key Dates

      • Deadline for Submission: 15th January, 2027
      • First Reviews Due: 15th April, 2027
      • Revised Manuscript Due: 15th June, 2027
      • Final Decision: 15th August, 2027

https://link.springer.com/journal/12880