AI for Lesion Detection in Musculoskeletal Radiology

Abstract

This review provides an overview of the latest trends in lesion detection using AI in musculoskeletal imaging. It describes the types of deep learning networks used in detection AI and briefly explains their principles. Fracture-detection AI has shown improved sensitivity and reduced reporting time in multiple meta-analyses, and real-world validation in clinical settings has begun. Although many AIs have been developed to detect joint injuries and degenerative changes in MRI and CT/MRI detection models for bone metastasis and multiple myeloma, they have not yet reached a robust validation stage. Achieving clinical value requires attention to explainability, external validation and post-market monitoring, Picture Archiving Communicating System (PACS)-level integration, and legal and ethical issues and, therefore, proactive adoption by radiology professionals.