Validation of a Deep Learning Tool for Opportunistic Osteoporosis Screening Using Routine Non-Contrast CT Scans

Prospective Clinical Validation Study of a Deep Learning Model for Opportunistic Osteoporosis Screening Based on Non-Contrast CT Scans

The goal of this clinical trial is to test if an artificial intelligence (AI) tool called DeepBMD can accurately identify people at high risk for osteoporosis using routine chest or abdomen CT scans. The main questions it aims to answer are:

  1. Can the DeepBMD tool correctly identify people who have osteoporosis compared to the standard bone density test, dual-energy X-ray absorptiometry (DXA)?
  2. Is it practical to use this AI tool in real-world hospital settings to find and contact high-risk patients? Researchers will use the DeepBMD tool to analyze existing CT scans. If the tool flags a patient as high risk, researchers will call them to invite them for a standard bone density test (DXA).

Participants will:

  1. Have their existing chest or abdomen CT scan analyzed by the DeepBMD AI tool;
  2. Receive a phone call from the research team if identified as high risk;
  3. Visit the clinic for a free standard bone density test (DXA) if they agree to participate.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

    • Hubei
      • Wuhan, Hubei, China, 430022
        • Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients who underwent non-contrast CT at our hospital (Union Hospital, Tongji Medical College, Huazhong University of Science and Technology) and were identified as high-risk for osteoporosis by the DeepBMD model.

Description

Inclusion Criteria:

  • Underwent non-contrast CT at our institution, with qualified image quality and no severe artifacts;
  • Identified as high-risk for osteoporosis by the DeepBMD model;
  • Had valid contact information available in the PACS, possessed normal cognitive and communication abilities, and was able to cooperate with telephone follow-ups and on-site examinations;
  • Voluntarily participated in the study, was able to sign a written informed consent form on-site, and agreed to undergo DXA examination.

Exclusion Criteria:

  • Severe spinal deformity, postoperative spinal internal fixation, malignant bone metastasis, or severe osteolytic lesions that may interfere with measurements;
  • A confirmed diagnosis of osteoporosis with ongoing standardized treatment;
  • Inability to be contacted, explicit refusal of follow-up, or inability to visit the hospital for informed consent signing and DXA examination.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
High-risk patients for osteoporosis identified by DeepBMD model
Patients who underwent routine chest or abdominal CT scans and were identified as high risk for osteoporosis by the DeepBMD AI model. These participants will be contacted via telephone, invited to the clinic, and undergo a free DXA scan to verify bone mineral density.
The DeepBMD model is applied to routine chest or abdominal CT scans to identify patients at high risk for osteoporosis. This is a non-invasive image analysis used solely for screening and recruitment purposes, not as a therapeutic intervention.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic performance of DeepBMD model for osteoporosis screening
Time Frame: Concurrent with the DXA validation visit following the CT analysis (within 7 days).
The diagnostic performance of the DeepBMD model will be evaluated by comparing its predictions against the gold standard Dual-energy X-ray Absorptiometry (DXA). Specifically, we will calculate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the Area Under the Receiver Operating Characteristic Curve (AUC) for identifying patients with osteoporosis.
Concurrent with the DXA validation visit following the CT analysis (within 7 days).

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Feasibility of the DeepBMD screening and recall workflow
Time Frame: At the end of recruitment
It will be assessed by calculating the proportion of patients identified as high-risk by DeepBMD who successfully complete the telephone follow-up and undergo the confirmatory DXA scan within the scheduled timeframe. We will also record the reasons for refusal or loss to follow-up to evaluate the acceptability of this AI-driven screening pathway.
At the end of recruitment

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Investigators

  • Principal Investigator: Fan Yang, PhD, MD, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 28, 2026

Primary Completion (Actual)

August 28, 2026

Study Completion (Estimated)

September 1, 2026

Study Registration Dates

First Submitted

July 22, 2026

First Submitted That Met QC Criteria

July 27, 2026

First Posted (Actual)

July 31, 2026

Study Record Updates

Last Update Posted (Actual)

September 3, 2026

Last Update Submitted That Met QC Criteria

September 1, 2026

Last Verified

September 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • UHCT260668

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

De-identified individual participant data (IPD) will be made available to researchers who provide a methodologically sound proposal. The shared data will include the demographic information, DeepBMD screening results, and confirmatory DXA T-scores used in the study analyses. Requests should be directed to the corresponding author via email. Data will be available for non-commercial academic research purposes only. Applicants must sign a data access agreement prior to receiving the data.

IPD Sharing Time Frame

Data will be available beginning 3 months following article publication and ending 36 months following article publication.

IPD Sharing Access Criteria

Researchers who provide a methodologically sound proposal for specific research questions related to osteoporosis screening or AI diagnostics will be granted access. Approved researchers will have access to the de-identified dataset containing patient demographics, imaging analysis results, and clinical outcomes. Access will be granted via secure email transfer after signing a data use agreement.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ANALYTIC_CODE

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.