Using Computer Learning to Estimate Bone Age From Abdominal CT Scans
Tests a computer tool that estimates bone age from existing abdominal CT scans in adults.
Plain-language title generated from the registry record
Registry title: Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning
Key facts
- Where
- Beijing, ChinaSee the site
AI summary: who the study is looking for
From the trial's eligibility criteria. Machine-read from the registry text on this page, so it can be wrong. The registry text below and the trial team decide who can take part.
The study uses past abdominal CT scans of adults to estimate bone age and spot early signs of osteoporosis. No new scans are needed.
- Age
- Over 18 years (minimum age 18 Years).
- Sex
- All sexes can join.
- Earlier treatment
- Must have had a routine noncontrast abdominal CT scan for a non-orthopedic reason. The scan must fully include the proximal femur (top of the thigh bone).
Main reasons for not taking part
- CT scan with poor image quality or severe artifacts
- History of hip surgery or internal fixation devices
- Bone tumor in the proximal femur
- Severe hip deformity or prior fracture affecting the proximal femur
- Pediatric patients or pregnant individuals (if applicable)
Read by machine on 9 November 2026.
Where in China
- CT machineBeijing, ChinaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Peking University People's Hospital
- Trial code
- NCT07162168
- Record updated
- 3 December 2025
- Original description
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.Read full original descriptionShow less
Development and Evaluation of a Deep Learning-Based Model for Automated Osteoporosis Assessment Using CT Images
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.
Inclusion: 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: Severe hip deformity or prior fractures affecting the proximal femur; History of hip surgery or presence of internal fixation devices; CT scans with poor image quality or severe artifacts that precluded accurate analysis; Presence of bone tumors in the proximal femur; Pediatric patients or pregnant individuals (if applicable).