Bone AgingNCT07162168
Automated Bone Age Estimation From Noncontrast Abdominal CT Using Deep Learning
An observational study of Bone Aging and Osteoporosis Diagnosis, run by Peking University People's Hospital at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Bone Aging, Osteoporosis Diagnosis
- How many people
- 3,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2024to 1 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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).
Who may be able to join
- 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).
Who cannot join
- 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).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
CT machine
Beijing
yuhui Kou, M.D, 86-13146213332, yuhuikou@bjmu.edu.cn
About this study
Official title: 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.
- Conditions
- Bone Aging, Osteoporosis Diagnosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Retrospective analysis of CT scans acquired between Sep 01.2024 to Oct 01.2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Sep 2025
- Study start1 Sep 2024
- Primary completion1 Sep 2027 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bone AgingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yuhui Kou, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07162168
- Sponsor's protocol ID
- 2024PHB388-001
- Status last verified
- Nov 2025
- First submitted
- 29 Aug 2025
- Last updated
- 3 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Our Research has not been finished yet.
Study at a glance
From the registry record, last updated by the sponsor on 3 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Evaluation of a Deep Learning-Based Model for Automated Osteoporosis Assessment Using CT Images
- ClinicalTrials.gov ID
- NCT07162168
- Acronym / sponsor ID
- 2024PHB388-001
- Conditions
- Bone Aging, Osteoporosis Diagnosis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- 1 Sep 2024 (actual)
- Primary completion
- 1 Sep 2027 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 9 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 9
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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).
Key inclusion criteria (5 listed)
- 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).
Key exclusion criteria (5 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Dec 2025.
- Individual participant dataSharing planned: No. Our Research has not been finished yet.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Bone Aging and Osteoporosis Diagnosis, run by Peking University People's Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 3 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact hanwen Cheng, M.D (86-19541080926, chenghanwen1998@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Radiomics-Based Bone Age Prediction Model (time frame: Retrospective analysis of CT scans acquired between Sep 01.2024 to Oct 01.2025). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Dec 2025.