Actively Recruiting

Age: 18Years +
All Genders
Healthy Volunteers
NCT07162168

Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning

Led by Peking University People's Hospital · Updated on 2025-12-03

3000

Participants Needed

1

Research Sites

169 weeks

Total Duration

On this page

AI-Summary

What this Trial Is About

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.

CONDITIONS

Official Title

Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning

Who Can Participate

Age: 18Years +
All Genders
Healthy Volunteers

Eligibility Criteria

Eligible

You may qualify if you...

  • 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).
Not Eligible

You will not qualify if you...

  • 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).

AI-Screening

AI-Powered Screening

Complete this quick 3-step screening to check your eligibility

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Trial Site Locations

Total: 1 location

1

CT machine

Beijing, China

Actively Recruiting

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Research Team

H

hanwen Cheng, M.D

CONTACT

How is the study designed?

Study Type

OBSERVATIONAL

Masking

N/A

Allocation

N/A

Model

N/A

Primary Purpose

N/A

Number of Arms

9

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