School of Mechanical Engineering, The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.
PerSimiO Ltd, Beer-Sheva, Israel.
J Bone Miner Res. 2023 Jun;38(6):876-886. doi: 10.1002/jbmr.4805. Epub 2023 Apr 19.
Autonomous finite element analyses (AFE) based on CT scans predict the biomechanical response of femurs during stance and sidewise fall positions. We combine AFE with patient data via a machine learning (ML) algorithm to predict the risk of hip fracture. An opportunistic retrospective clinical study of CT scans is presented, aimed at developing a ML algorithm with AFE for hip fracture risk assessment in type 2 diabetic mellitus (T2DM) and non-T2DM patients. Abdominal/pelvis CT scans of patients who experienced a hip fracture within 2 years after an index CT scan were retrieved from a tertiary medical center database. A control group of patients without a known hip fracture for at least 5 years after an index CT scan was retrieved. Scans belonging to patients with/without T2DM were identified from coded diagnoses. All femurs underwent an AFE under three physiological loads. AFE results, patient's age, weight, and height were input to the ML algorithm (support vector machine [SVM]), trained by 80% of the known fracture outcomes, with cross-validation, and verified by the other 20%. In total, 45% of available abdominal/pelvic CT scans were appropriate for AFE (at least 1/4 of the proximal femur was visible in the scan). The AFE success rate in automatically analyzing CT scans was 91%: 836 femurs we successfully analyzed, and the results were processed by the SVM algorithm. A total of 282 T2DM femurs (118 intact and 164 fractured) and 554 non-T2DM (314 intact and 240 fractured) were identified. Among T2DM patients, the outcome was: Sensitivity 92%, Specificity 88% (cross-validation area under the curve [AUC] 0.92) and for the non-T2DM patients: Sensitivity 83%, Specificity 84% (cross-validation AUC 0.84). Combining AFE data with a ML algorithm provides an unprecedented prediction accuracy for the risk of hip fracture in T2DM and non-T2DM populations. The fully autonomous algorithm can be applied as an opportunistic process for hip fracture risk assessment. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
基于 CT 扫描的自主有限元分析（AFE）可预测股骨在站立和侧倾位置时的生物力学反应。我们通过机器学习（ML）算法将 AFE 与患者数据相结合，以预测髋部骨折的风险。本研究是一项机会性回顾性临床研究，旨在开发一种基于 CT 扫描的 AFE 和 ML 算法，用于评估 2 型糖尿病（T2DM）和非 T2DM 患者的髋部骨折风险。从一家三级医疗中心的数据库中检索到在索引 CT 扫描后 2 年内发生髋部骨折的患者的腹部/骨盆 CT 扫描。从编码诊断中确定了至少在索引 CT 扫描后 5 年内无已知髋部骨折的患者的对照组。从患者中识别出有/无 T2DM 的扫描/诊断。所有股骨在三种生理负荷下进行 AFE。将 AFE 结果、患者年龄、体重和身高输入到 ML 算法（支持向量机[SVM]）中，该算法使用 80%的已知骨折结果进行训练，并进行交叉验证，然后使用另外 20%进行验证。总共，45%的可用腹部/骨盆 CT 扫描适合 AFE（至少在扫描中可见股骨近端的 1/4）。自动分析 CT 扫描的 AFE 成功率为 91%：成功分析了 836 个股骨，结果由 SVM 算法处理。总共确定了 282 个 T2DM 股骨（118 个完整和 164 个骨折）和 554 个非 T2DM 股骨（314 个完整和 240 个骨折）。在 T2DM 患者中，结果为：敏感性 92%，特异性 88%（交叉验证 AUC 为 0.92），而非 T2DM 患者为：敏感性 83%，特异性 84%（交叉验证 AUC 为 0.84）。将 AFE 数据与 ML 算法相结合，可以为 T2DM 和非 T2DM 人群的髋部骨折风险提供前所未有的预测准确性。完全自主的算法可以作为髋部骨折风险评估的机会性过程。
