Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning

Recruiting now

Conditions studied: Bone Aging, Osteoporosis Diagnosis

In brief

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.

Key facts

Study ID

NCT07162168

Run by

Peking University People's Hospital

People needed

3000

Starts

2024-09-01

Expected to finish

2027-12-01

Last updated by the study team

2025-12-03

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• 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).

You may not qualify if…

• 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).

Where it is running

• CT machine — Beijing, China (enrolling)

Full record on ClinicalTrials.gov

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