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.

Easily missed pathologies of the musculoskeletal system in the emergency radiology setting

“…There are now numerous AI-based solutions on the market de-signed to assist radiologists in reporting, akin to a 'four-eyes principle'. Applications of AI for image interpretation in the musculoskeletal region consist of the determination of body composition measurements, bone age, identification of fractures, screening for osteoporosis, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis [44,45]. The number of publications per year in PubMed using the keywords ('artificial intelligence' and 'fracture' and 'radiology') has increased steadily from 30 publications in 2019 to 151 publications in 2023, and several AI algorithms, specifically deep learning algorithms, have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians [46,47,48].…”

Pioneering Data Processing for Convolutional Neural Networks to Enhance the Diagnostic Accuracy of Traditional Chinese Medicine Pulse Diagnosis for Diabetes

“…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.…”