Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning
Recruiting · NCT07162168 · Observational (researchers observe without assigning treatment) · Lead sponsor: Peking University People's Hospital
View the official record on ClinicalTrials.gov →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.
Who can take part
Age range
18 Years and older
Sex
All (male and female)
Healthy volunteers
Yes - healthy volunteers may be accepted
Phase
Not specified
Study type
Observational (researchers observe without assigning treatment)
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).
Where it is running
1 location listed.
- CT machine - Beijing, China