Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning

NCT07162168 | Recruiting

• 1 other identifier: 2024PHB388-001
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief Summary

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

Bone Aging; Osteoporosis Diagnosis

Outcome Measures

Primary Outcomes (1)

• Radiomics-Based Bone Age Prediction Model

  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.

  Retrospective analysis of CT scans acquired between Sep 01.2024 to Oct 01.2025

Study Arms (9)

Peking University People's Hospital cohort

No intervention

Shandong Cohort

No intervention

Canton Cohort

No intervention

Guizhou cohort

No intervention

Hunan Cohort

No intervention

Inner Mongolia Cohort

No intervention

Shaanxi Cohort

No intervention

Shandong Cohort2

No intervention

Other province Cohort

No intervention

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This retrospective study included about 3,000 adult participants (aged over 18 years) who underwent noncontrast abdominal CT scans that fully covered the proximal femur across multiple regions in China. Participants with poor image quality, prior hip surgery or internal fixation, bone tumors, severe hip deformities, or prior proximal femur fractures were excluded. All scans were performed for non-orthopedic indications. The study was approved by the Institutional Ethics Committee (approval number: 2024PHB388-001).

You may qualify if:

• 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).

You may not qualify if:

• 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).

Sponsors & Collaborators

• Peking University People's Hospital: lead

Study Sites (1)

CT machine

Beijing, China

RECRUITING

Central Study Contacts

hanwen Cheng, M.D

CONTACT

86-19541080926; chenghanwen1998@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Research Fellow

Study Record Dates

• First Submitted: August 29, 2025
• First Posted: September 9, 2025
• Study Start: September 1, 2024
• Primary Completion (Estimated): September 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: December 3, 2025

Record last verified: 2025-11

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)