Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review

Abstract Introduction: Worldwide, 158 million people over the age of 50 are estimated to have high risk of osteoporotic fractures. It is important to prevent the occurrence of primary fractures by diagnosing and treating osteoporosis at an early stage. Dual energy absorptiometry (DEXA) is one of the preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture to some extent. However, the DEXA test may be difficult to implement easily in some developing countries and fractures have been observed before patients underwent DEXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using AI or ML, organize the results of each study, and analyze the usefulness of this technology. Methods: PubMed Central, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched to identify relevant studies published up to June 2022 with English language restriction. The following search terms were used ("hip fractures"[MeSH Terms] OR ("hip"[All Fields] AND "fractures"[All Fields]) OR "hip fractures"[All Fields] OR ("hip"[All Fields] AND "fracture"[All Fields]) OR "hip fracture"[All Fields]) AND ("artificial intelligence"[MeSH Terms] OR ("artificial"[All Fields] AND "intelligence"[All Fields]) OR "artificial intelligence"[All Fields]). Results: 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The AUC of AI prediction model for hip fracture risk was 0.39–0.96. The accuracy of AI prediction model for hip fracture risk was 70.26–90%. Conclusion: We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, in order to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.