A comprehensive review of AI methods in upper extremity/limb bone fracture detection

Abstract

Accurate detection of bone fractures is crucial for patient care, however, the traditional manual review of medical images like X-rays, Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRIs), and ultrasounds is time-consuming and labor-intensive. The shortage of clinicians, limited access to expert radiologists, and heavy workloads increase the risk of errors, which can slow down patients recovery. Artificial Intelligence (AI) models like Faster R-CNN have shown significant diagnostic accuracy (ACC) and sensitivity (SEN), often outperforming on-call radiologists in detecting complex fracture types. For example, Faster R-CNN has achieved SEN exceeding 90% in distal radius fracture detection. However, despite these advancements, AI-driven fracture detection systems still face several challenges, including the need for extensive annotated datasets, variability in imaging quality across clinical settings, potential biases in model training, and concerns regarding the interpretability and reliability of AI-generated predictions. This review provides a comprehensive analysis of recent advancements and limitations in AI-based fracture detection, offering quantitative insights into model performance. By examining these aspects, the study highlights the importance of integrating AI systems into clinical workflows, while addressing existing barriers to their widespread adoption. This analysis underscores AI’s potential to enhance diagnostic efficiency, reduce human error, and improve patient outcomes.