Deep learning approaches for osteoarthritis diagnosis via patient activity data and medical imaging: A review.

Osteoarthritis (OA), a prevalent degenerative joint disease, triggers significant global impairment. Timely intervention relies on accurate early diagnosis. Recent advancements employ deep learning (DL) models that integrate patient clinical data with Imaging techniques like X-rays, MRI, and CT scans, enhancing OA detection and assessment. This review surveys recent research on DL techniques for OA detection, highlighting the development of convolutional neural networks (CNNs) and innovative architectures. CNNs analyze medical images, automatically extracting features indicative of OA progression. Models combining patient demographic information, clinical history, symptoms, joint biomechanics, and imaging data show improved OA onset and progression prediction compared to imaging alone. Transfer learning fine-tuning CNNs pre-trained on datasets like ImageNet enhances feature extraction and classification accuracy. Hybrid models, merging CNNs with traditional machine learning (ML) methods like SVM, capitalize on the strengths of both approaches. Despite progress, challenges include reliance on training data volume and quality, class imbalance, and limited model generalization across diverse datasets. DL holds promise for automated and objective OA diagnosis, severity grading, treatment planning, and prognostication. However, further research with multi-modal datasets and optimized model architectures is essential to realize its clinical utility and generalizability for OA management fully. This review synthesizes the field's current state, outlining future directions in this evolving application at the intersection of artificial intelligence and musculoskeletal health. [ABSTRACT FROM AUTHOR]