CT machine
Beijing, China
Location status: Recruiting
Recruiting
NCT Number: NCT07162168
Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning
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.
Recruiting
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Conditions
Age range
18 year and older
Sex eligibility
All sexes
Study type
Observational
Primary location
Who can participate
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
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).
Treatment and study plan
Primary outcomes
-
Radiomics-Based Bone Age Prediction Model
Time frame: 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.
Study contacts
Contact information is provided by the study sponsor or research team.
Sponsors and collaborators
Lead sponsor
Peking University People's Hospital
Other
Registry information
Official study title
Development and Evaluation of a Deep Learning-Based Model for Automated Osteoporosis Assessment Using CT Images
Important dates
- Study start
- 2024
- Primary completion
- 2027
- Study completion
- 2027
- First posted
- Sep 9, 2025
- Registry last updated
- Dec 3, 2025
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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