A comparative study of bone density in elderly people measured with AI and QCT

作者：Min Guo, Yu Zhang, Xiuying Gu, Xuhui Liu, Fei Peng, Zongjun Zhang, Jing Mei, Yingxia Fu · 发表于：Frontiers in Artificial Intelligence · 年份：2025 · DOI：10.3389/frai.2025.1582960 · 被引用次数：10 · 研究领域：Bone health and osteoporosis research、Artificial Intelligence in Healthcare and Education、Parathyroid Disorders and Treatments

Background 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. Objective 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. Methods 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...