Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Bone Aging, Osteoporosis Diagnosis. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Development and Evaluation of a Deep Learning-Based Model for Automated Osteoporosis Assessment Using CT Images
Overview
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.
Primary outcome measures
- Radiomics-Based Bone Age Prediction Model [Time frame: Retrospective analysis of CT scans acquired between Sep 01.2024 to Oct 01.2025]
Eligibility criteria
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).
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- CT machine — Beijing
Identifiers
NCT: NCT07162168 · 2024PHB388-001