Bone Aging, Osteoporosis Diagnosis
Conditions
Brief summary
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.
Interventions
None listed
Sponsors
Peking University People's Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* 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).
Exclusion criteria
* 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).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Radiomics-Based Bone Age Prediction Model | 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. |
Countries
China
Contacts
Primary Contacthanwen Cheng, M.D
Outcome results
None listed