Cha Yonghan, Kim Jung-Taek, Kim Jin-Woo, Seo Sung Hyo, Lee Sang-Yeob, Yoo Jun-Il
Department of Orthopedic Surgery, Daejeon Eulji Medical Center, Eulji University School of Medicine, Daejeon, Korea.
Department of Orthopedic Surgery, Ajou Medical Center, Ajou University School of Medicine, Suwon, Korea.
J Bone Metab. 2023 Aug;30(3):245-252. doi: 10.11005/jbm.2023.30.3.245. Epub 2023 Aug 31.
Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology.
The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including "hip fractures" AND "artificial intelligence".
A total of 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 area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%.
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, 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.
双能X线吸收法（DXA）是骨质疏松症筛查或诊断的首选方法，并且可以预测髋部骨折风险。然而，在一些发展中国家，DXA检测难以轻易实施，而且在患者进行DXA检测之前就已观察到骨折情况。本系统评价的目的是检索使用人工智能（AI）或机器学习预测髋部骨折风险的研究，整理每项研究的结果，并分析该技术的实用性。
检索了PubMed、OVID Medline、Cochrane协作网图书馆、科学引文索引、EMBASE和美国医疗保健研究与质量局数据库，检索词包括“髋部骨折”和“人工智能”。
本研究共纳入7项研究。这7项研究纳入的受试者总数为330,０99名。其中3项研究仅纳入女性，4项研究纳入了男性和女性。一项研究在骨折和未骨折患者进行1:1匹配后进行了AI训练。AI预测模型预测髋部骨折风险的曲线下面积为0.39至0.96。AI预测模型预测髋部骨折风险的准确率为70.26%至90%。
我们认为通过AI模型预测髋部骨折风险将有助于在骨质疏松症患者中筛选出骨折风险高的患者。然而，要将AI模型应用于临床情况下髋部骨折风险的预测，有必要识别数据集和AI模型的特征，并在进行适当验证后使用。
