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

NCT07739628 | Enrolling By Invitation

• 1 other identifier: UHCT260668
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

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

Conditions

Osteoporosis

Keywords

Osteoporosis; Opportunistic Screening; Deep Learning; Bone Mineral Density; Computed Tomography (CT)

Outcome Measures

Primary Outcomes (1)

• Diagnostic performance of DeepBMD model for osteoporosis screening

  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 Outcomes (1)

• Feasibility of the DeepBMD screening and recall workflow

  At the end of recruitment

Study Arms (1)

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.

Diagnostic Test: DeepBMD model for osteoporosis risk screening

Interventions

DeepBMD model for osteoporosis risk screening DIAGNOSTIC_TEST

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.

High-risk patients for osteoporosis identified by DeepBMD model

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: lead

Study Sites (1)

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430022, China

Location

Related Publications (6)

• Jang S, Graffy PM, Ziemlewicz TJ, Lee SJ, Summers RM, Pickhardt PJ. Opportunistic Osteoporosis Screening at Routine Abdominal and Thoracic CT: Normative L1 Trabecular Attenuation Values in More than 20 000 Adults. Radiology. 2019 May;291(2):360-367. doi: 10.1148/radiol.2019181648. Epub 2019 Mar 26.

  PMID: 30912719 BACKGROUND

• Wang P, She W, Mao Z, Zhou X, Li Y, Niu J, Jiang M, Huang G. Use of routine computed tomography scans for detecting osteoporosis in thoracolumbar vertebral bodies. Skeletal Radiol. 2021 Feb;50(2):371-379. doi: 10.1007/s00256-020-03573-y. Epub 2020 Aug 7.

  PMID: 32767060 BACKGROUND

• Smith AD. Screening of Bone Density at CT: An Overlooked Opportunity. Radiology. 2019 May;291(2):368-369. doi: 10.1148/radiol.2019190434. Epub 2019 Mar 26. No abstract available.

  PMID: 30917293 BACKGROUND

• Zeng Q, Li N, Wang Q, Feng J, Sun D, Zhang Q, Huang J, Wen Q, Hu R, Wang L, Ma Y, Fu X, Dong S, Cheng X. The Prevalence of Osteoporosis in China, a Nationwide, Multicenter DXA Survey. J Bone Miner Res. 2019 Oct;34(10):1789-1797. doi: 10.1002/jbmr.3757. Epub 2019 Aug 29.

  PMID: 31067339 BACKGROUND

• Cheng X, Zhao K, Zha X, Du X, Li Y, Chen S, Wu Y, Li S, Lu Y, Zhang Y, Xiao X, Li Y, Ma X, Gong X, Chen W, Yang Y, Jiao J, Chen B, Lv Y, Gao J, Hong G, Pan Y, Yan Y, Qi H, Ran L, Zhai J, Wang L, Li K, Fu H, Wu J, Liu S, Blake GM, Pickhardt PJ, Ma Y, Fu X, Dong S, Zeng Q, Guo Z, Hind K, Engelke K, Tian W; China Health Big Data (China Biobank) project investigators. Opportunistic Screening Using Low-Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study. J Bone Miner Res. 2021 Mar;36(3):427-435. doi: 10.1002/jbmr.4187. Epub 2020 Nov 4.

  PMID: 33145809 BACKGROUND

• Lin X, Xiong D, Peng YQ, Sheng ZF, Wu XY, Wu XP, Wu F, Yuan LQ, Liao EY. Epidemiology and management of osteoporosis in the People's Republic of China: current perspectives. Clin Interv Aging. 2015 Jun 25;10:1017-33. doi: 10.2147/CIA.S54613. eCollection 2015.

  PMID: 26150706 BACKGROUND

MeSH Terms

Conditions

Osteoporosis

Condition Hierarchy (Ancestors)

Bone Diseases, Metabolic; Bone Diseases; Musculoskeletal Diseases; Metabolic Diseases; Nutritional and Metabolic Diseases

Study Officials

• Fan Yang, PhD, MD

  Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 7 Days
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor and Chief Physician

Study Record Dates

• First Submitted: July 22, 2026
• First Posted: July 31, 2026
• Study Start: July 1, 2026
• Primary Completion: August 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: July 31, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ANALYTIC CODE
• Time Frame: Data will be available beginning 3 months following article publication and ending 36 months following article publication.
• 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.

Locations

CN (1)