The Role of Artificial Intelligence in the Identification and Evaluation of Bone Fractures

Abstract

Artificial intelligence (AI), particularly deep learning, has made enormous strides in medical imaging analysis. In the field of musculoskeletal radiology, deep-learning models are actively being developed for the identification and evaluation of bone fractures. These methods provide numerous benefits to radiologists such as increased diagnostic accuracy and efficiency while also achieving standalone performances comparable or superior to clinician readers. Various algorithms are already commercially available for integration into clinical workflows, with the potential to improve healthcare delivery and shape the future practice of radiology. In this systematic review, we explore the performance of current AI methods in the identification and evaluation of fractures, particularly those in the ankle, wrist, hip, and ribs. We also discuss current commercially available products for fracture detection and provide an overview of the current limitations of this technology and future directions of the field.

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“…Shiva Maleki Varnosfaderani et al (2024) [9] highlight the advancements in AI, particularly deep learning, for medical image analysis, with a specific focus on bone fracture identification and evaluation within musculoskeletal radiology. Their work emphasizes the improved diagnostic accuracy and efficiency offered by these AI methods, often surpassing human clinicians and already available in commercial products for clinical integration.…”