Development and Evaluation of a Deep Learning-Based Model for Automated Osteoporosis Assessment Using CT Images
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 3,000
- 试验地点
- 1
- 主要终点
- Radiomics-Based Bone Age Prediction Model
研究概览
简要总结
This study is a retrospective analysis that uses abdominal CT scans, which were originally taken for other medical reasons, to estimate bone age. By applying advanced deep learning methods, the investigators aim to develop a tool that can evaluate bone health and detect early signs of osteoporosis without requiring additional scans or radiation. This approach may help doctors better understand bone aging, improve screening for bone weakness, and provide patients with more personalized information about their bone health.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Adults aged over 18 years.
- •Underwent routine noncontrast abdominal CT scans.
- •CT scans fully included the proximal femur.
- •Scans were performed for non-orthopedic clinical indications.
- •Provided necessary demographic information (e.g., age, sex).
排除标准
- •CT scans with poor image quality or severe artifacts that precluded accurate analysis.
- •History of hip surgery or presence of internal fixation devices.
- •Presence of bone tumors in the proximal femur.
- •Severe hip deformity or prior fractures affecting the proximal femur.
- •Pediatric patients or pregnant individuals (if applicable).
结局指标
主要结局
Radiomics-Based Bone Age Prediction Model
时间窗: Retrospective analysis of CT scans acquired between Sep 01.2024 to Oct 01.2025
Extraction of radiomics features from abdominal CT images of the proximal femur and development of a machine learning model to estimate biological bone age. The performance of the model will be evaluated by comparing predicted bone age with chronological age.
次要结局
未报告次要终点
研究者
Yuhui Kou
Research Fellow
Peking University People's Hospital