Publications

Machine learning (ML) approaches using quantitative imaging biomarkers show promise for automated OA classification, but their reliability under imperfect image segmentation remains unclear. This study evaluated the robustness of cartilage-based radiodensity and morphological features derived from MRI-registered CT scans against simulated segmentation errors.

This review highlights cartilage–sensory nerve crosstalk as a key mechanism underlying osteoarthritis pain, moving beyond a structure-centric view of disease progression. Mechanistic insights into neuroinflammatory and mechanosensitive pathways support the development of biomarkers for pain phenotyping and patient stratification.

The increasing dimensionality of healthcare datasets presents major challenges for clinical data analysis and interpretation. This study introduces a scalable ensemble feature selection (FS) strategy optimized for multi-biometric healthcare datasets aiming to: address the need for dimensionality reduction, identify the most significant features, improve machine learning models’ performance, and enhance interpretability in a clinical context.

Osteoarthritis (OA) is a common joint disease affecting people worldwide, notably impacting quality of life due to joint pain and functional limitations. This study explores the potential of radiomics — quantitative image analysis combined with machine learning — to enhance knee OA diagnosis.