Diagnostic Accuracy of Artificial Intelligence for Detection of Rib Fracture on X-ray and Computed Tomography Imaging: A Systematic Review

Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload and serve as a tool to improve accurate diagnosis. To date, no reviews have been performed synthesizing identification of rib fracture data on AI and its diagnostic performance on X-ray and CT scans of rib fractures and its comparison to physicians. The objectives of this study are to analyze the performance of artificial intelligence in diagnosing rib fracture on X-ray and computed tomography (CT) scan using multiple clinical studies and to compare it to that of physicians findings of rib fracture. A literature search was conducted on PubMed and Embase for articles regarding the use of artificial intelligence for the detection of rib fractures up until July 2024. AI model, number of cases, sensitivity, and comparison to physicians data was collected. A total of 29 studies, comprising 125,364 cases, were included in this review. The pooled sensitivity of AI models was 0.853. Nineteen of these studies compared their results to radiologists, orthopedic surgeons, or anesthesiologists, totalling 61 physicians. Of these 19 studies, the radiologists had a pooled sensitivity of 0.750. The sensitivity of AI in these studies by comparison was 0.840. The results suggest that artificial intelligence has a promising role in detecting rib fractures on X-ray and CT scans. In our interpretation, the performance of artificial intelligence is similar to, or better than, that of physicians, alluding to its encouraging potential in a clinical setting as it may reduce physician workload, improve reading efficiency, and lead to better patient outcomes.