Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan, R.O.C.
Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C.
J Med Syst. 2024 Jan 13;48(1):12. doi: 10.1007/s10916-023-02030-2.
A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.
开发了一种深度学习模型，通过胸部 X 射线 (CXR) 特征以高精度识别骨质疏松症，在内部和外部验证中均具有显著的预后意义，可以识别出全因死亡率较高的个体。这种人工智能 (AI) 驱动的 CXR 策略可以作为骨质疏松症的早期检测筛查工具。本研究旨在开发一种深度学习模型 (DLM) 通过 CXR 特征识别骨质疏松症，并研究其性能和临床意义。本研究从学术医疗中心收集了 48353 份具有相应双能 X 射线吸收法 (DXA) T 评分的 CXR。其中，35633 份 CXR 用于识别 CXR-骨质疏松症 (CXR-OP)。另外 12720 份 CXR 用于验证性能，通过接受者操作特征曲线下面积 (AUC) 进行评估。此外，还测试了 CXR-OP 以评估死亡率的长期风险，通过 Kaplan-Meier 生存分析和 Cox 比例风险模型进行评估。利用 CXR 的 DLM 在内部和外部验证中分别获得了 0.930 和 0.892 的 AUC。在进行了 DXA 且 CXR-OP 的组中，全因死亡率的风险更高（风险比 [HR] 2.59，95%CI：1.83-3.67），而在未进行 DXA 且分类为 CXR-OP 的组中，全因死亡率也更高（HR：1.67，95%CI：1.61-1.72）在内部验证集中。外部验证集也产生了类似的结果。我们的 DLM 使用 CXR 进行骨质疏松症的早期检测，帮助医生识别风险人群。它具有显著的预后意义，可以提高生活质量并降低死亡率。AI 驱动的 CXR 策略可以作为一种筛查工具。
