Guo Min, Zhang Yu, Gu XinXin, Liu Xuhui, Peng Fei, Zhang Zongjun, Jing Mei, Fu Yingxia
Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.
Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing, China.
Front Artif Intell. 2025 Jul 23;8:1582960. doi: 10.3389/frai.2025.1582960. eCollection 2025.
Osteoporosis, a systemic skeletal disorder characterized by deteriorated bone microarchitecture and low bone mass, poses substantial fracture risks to aging populations globally. Early detection of reduced bone mineral density (BMD) through opportunistic screening is critical for preventing fragility fractures. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis, many patients have not undergone screening with this technique. Therefore, developing an automated tool that can diagnose bone density through routine chest and abdominal CT examinations is highly important. With advancements in technology and the accumulation of clinical data, the role of bone density artificial intelligence (AI) in the diagnosis and management of osteoporosis is becoming increasingly significant.
First to validate the diagnostic equivalence of AI-based BMD prediction against quantitative CT (QCT) reference standards, second to assess inter-device measurement consistency across multi-vendor CT systems (Siemens, GE, Philips). Ultimately, the objective is to determine the clinical utility of AI-derived BMD for osteoporosis classification.
In this retrospective multicenter study, paired CT/QCT datasets from 702 patients (2019-2022) were analyzed. The accuracy, sensitivity, and specificity of an Bone Density AI model were evaluated by comparing the predicted bone mineral density values from bone density AI with the measured values from QCT. Moreover, the consistency of lumbar spine BMD measurements between QCT and Bone Density AI on different devices was compared.
The AUC of Bone Density AI model in diagnosing osteoporosis was 0.822 (95% CI: 0.787-0.867, < 0.001), with an accuracy of 0.9456, sensitivity of 0.9601, and specificity of 0.9270, indicating good performance in predicting bone density. The consistency study between Bone Density AI and QCT for the vertebral BMD measurements revealed no statistically significant difference in values, suggesting no significant difference in performance between the two methods in measuring BMD. The linear regression fit between the values of QCT and Bone Density AI for measuring lumbar spine BMD with different equipment ranged from 0.88 to 0.96, indicating a high degree of consistency between the two measurement methods across devices.
This multicenter study pioneers a dual-validation framework to establish the clinical validity of deep learning-based BMD prediction algorithms using routine thoracic/abdominal CT scans. Our data suggest that AI-driven BMD quantification demonstrates non-inferior diagnostic accuracy to QCT while overcoming DXA's accessibility limitations. This technology enables cost-effective, radiation-free osteoporosis screening through routine CT repurposing, particularly beneficial for resource-constrained settings.
骨质疏松症是一种全身性骨骼疾病，其特征为骨微结构恶化和骨量降低，给全球老年人群带来了巨大的骨折风险。通过机会性筛查早期发现骨密度（BMD）降低对于预防脆性骨折至关重要。尽管双能X线吸收法（DXA）是诊断骨质疏松症的金标准，但许多患者尚未接受过该技术的筛查。因此，开发一种能够通过常规胸部和腹部CT检查诊断骨密度的自动化工具非常重要。随着技术的进步和临床数据的积累，骨密度人工智能（AI）在骨质疏松症的诊断和管理中的作用越来越显著。
首先验证基于AI的BMD预测与定量CT（QCT）参考标准的诊断等效性，其次评估跨多厂商CT系统（西门子、通用电气、飞利浦）的设备间测量一致性。最终目标是确定AI衍生的BMD在骨质疏松症分类中的临床效用。
在这项回顾性多中心研究中，分析了702例患者（2019 - 2022年）的配对CT/QCT数据集。通过比较骨密度AI预测的骨矿物质密度值与QCT测量值，评估骨密度AI模型的准确性、敏感性和特异性。此外，还比较了不同设备上QCT和骨密度AI之间腰椎BMD测量的一致性。
骨密度AI模型诊断骨质疏松症的AUC为0.822（95%CI：0.787 - 0.867，P<0.001），准确率为0.9456，敏感性为0.9601，特异性为0.9270，表明在预测骨密度方面表现良好。骨密度AI与QCT在椎体BMD测量方面的一致性研究显示，T值无统计学显著差异，表明两种方法在测量BMD方面的性能无显著差异。不同设备测量腰椎BMD时，QCT与骨密度AI的T值之间的线性回归拟合范围为0.88至0.96，表明两种测量方法在不同设备间具有高度一致性。
这项多中心研究开创了一个双重验证框架，以建立使用常规胸部/腹部CT扫描的基于深度学习的BMD预测算法的临床有效性。我们的数据表明，AI驱动的BMD量化显示出与QCT相当的诊断准确性，同时克服了DXA的可及性限制。这项技术通过常规CT的重新利用实现了具有成本效益的、无辐射的骨质疏松症筛查，对资源有限的环境尤其有益。
