Integrating Deep Learning in Musculoskeletal Radiology: Current Applications and Future Directions

Abstract

Background: Artificial intelligence (AI), particularly through deep learning techniques, is transforming musculoskeletal radiology. The integration of convolutional neural networks (CNNs) enhances the detection and classification of musculoskeletal conditions, addressing the rising demands of radiologists.

Methods: This review synthesizes the current literature on the applications of deep learning in musculoskeletal imaging. A narrative approach was employed, analyzing data from the PubMed database using general and specific search terms related to deep learning in radiology. Key clinical studies were selected based on their relevance to everyday practice.

Results: Recent advancements demonstrate that deep learning algorithms can accurately identify fractures, cartilage lesions, and ligament injuries, often achieving performance levels comparable to expert radiologists. Notably, CNNs have reached high diagnostic accuracy in detecting upper and lower extremity fractures, with some models outperforming human specialists. The review highlights automated assessments of osteoarthritis, spinal stenosis, and skeletal maturity, showcasing the potential for AI to streamline workflow and improve diagnostic accuracy.

Conclusion: The rise of deep learning in musculoskeletal radiology presents significant opportunities for enhancing diagnostic processes. While many algorithms exhibit expert-level performance, the need for thorough interpretation of imaging studies remains. As AI technology evolves, it is poised to play a critical role in the future of musculoskeletal radiology, potentially reshaping clinical practices.