Nguyen Huy Gia, Nguyen Dinh-Tan, Tran Thach Son, Ling Sai Ho, Ho-Pham Lan Thuc, Van Nguyen Tuan
School of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia.
Saigon Precision Medicine Research Center, Ho Chi Minh City, Vietnam.
Osteoporos Int. 2025 Aug 27. doi: 10.1007/s00198-025-07634-7.
Dual-energy X-ray absorptiometry (DXA) is the standard method for assessing areal bone mineral density (aBMD), diagnosing osteoporosis, and predicting fracture risk. However, DXA's availability is limited in resource-poor areas. This study aimed to develop an artificial intelligence (AI) system capable of estimating aBMD from standard radiographs.
The study was part of the Vietnam Osteoporosis Study, a prospective population-based research involving 3783 participants aged 18 years and older. A total of 7060 digital radiographs of the frontal pelvis and lateral spine were taken using the FCR Capsula XLII system (Fujifilm Corp., Tokyo, Japan). aBMD at the femoral neck and lumbar spine was measured with DXA (Hologic Horizon, Hologic Corp., Bedford, MA, USA). An ensemble of seven deep-learning models was used to analyze the X-rays and predict bone mineral density, termed "xBMD".
The correlation between xBMD and aBMD was evaluated using Pearson's correlation coefficients. The correlation between xBMD and aBMD at the femoral neck was strong ( = 0.90; 95% CI, 0.88-0.91), and similarly high at the lumbar spine ( = 0.87; 95% CI, 0.85-0.88). This correlation remained consistent across different age groups and genders. The AI system demonstrated excellent performance in identifying individuals at high risk for hip fractures, with area under the ROC curve (AUC) values of 0.96 (95% CI, 0.95-0.98) at the femoral neck and 0.97 (95% CI, 0.96-0.99) at the lumbar spine.
These findings indicate that AI can accurately predict aBMD and identify individuals at high risk of fractures. This AI system could provide an efficient alternative to DXA for osteoporosis screening in settings with limited resources and high patient demand. An AI system developed to predict aBMD from X-rays showed strong correlations with DXA ( = 0.90 at femoral neck; = 0.87 at lumbar spine) and high accuracy in identifying individuals at high risk for fractures (AUC = 0.96 at femoral neck; AUC = 0.97 at lumbar spine).
双能X线吸收法（DXA）是评估骨面积密度（aBMD）、诊断骨质疏松症和预测骨折风险的标准方法。然而，DXA在资源匮乏地区的可用性有限。本研究旨在开发一种能够从标准X线片估计aBMD的人工智能（AI）系统。
该研究是越南骨质疏松症研究的一部分，这是一项基于人群的前瞻性研究，涉及3783名18岁及以上的参与者。使用FCR Capsula XLII系统（日本东京富士胶片公司）拍摄了总共7060张骨盆正位和脊柱侧位的数字X线片。使用DXA（美国马萨诸塞州贝德福德市Hologic公司的Hologic Horizon）测量股骨颈和腰椎的aBMD。使用七个深度学习模型的集合来分析X线片并预测骨密度，称为“xBMD”。
使用Pearson相关系数评估xBMD与aBMD之间的相关性。股骨颈处xBMD与aBMD之间的相关性很强（r = 0.90；95% CI，0.88 - 0.91），腰椎处同样很高（r = 0.87；95% CI，0.85 - 0.88）。这种相关性在不同年龄组和性别中保持一致。该AI系统在识别髋部骨折高风险个体方面表现出色，股骨颈处的ROC曲线下面积（AUC）值为0.96（95% CI，0.95 - 0.98），腰椎处为0.97（95% CI，0.96 - 0.99）。
这些发现表明AI可以准确预测aBMD并识别骨折高风险个体。在资源有限且患者需求高的情况下，该AI系统可为骨质疏松症筛查提供一种有效的替代DXA的方法。一个用于从X线片预测aBMD的AI系统与DXA显示出很强的相关性（股骨颈处r = 0.90；腰椎处r = 0.87），并且在识别骨折高风险个体方面具有很高的准确性（股骨颈处AUC = 0.96；腰椎处AUC = 0.97）。
