Validation of a Deep Learning Tool for Opportunistic Osteoporosis Screening Using Routine Non-Contrast CT Scans
1 september 2026 bijgewerkt door: Yang Fan, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
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:
- Can the DeepBMD tool correctly identify people who have osteoporosis compared to the standard bone density test, dual-energy X-ray absorptiometry (DXA)?
- 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:
- Have their existing chest or abdomen CT scan analyzed by the DeepBMD AI tool;
- Receive a phone call from the research team if identified as high risk;
- Visit the clinic for a free standard bone density test (DXA) if they agree to participate.
Studie Overzicht
Toestand
Actief, niet wervend
Conditie
Interventie / Behandeling
Studietype
Observationeel
Inschrijving (Geschat)
100
Contacten en locaties
In dit gedeelte vindt u de contactgegevens van degenen die het onderzoek uitvoeren en informatie over waar dit onderzoek wordt uitgevoerd.
Studie Locaties
-
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Hubei
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Wuhan, Hubei, China, 430022
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
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Deelname Criteria
Onderzoekers zoeken naar mensen die aan een bepaalde beschrijving voldoen, de zogenaamde geschiktheidscriteria. Enkele voorbeelden van deze criteria zijn iemands algemene gezondheidstoestand of eerdere behandelingen.
Geschiktheidscriteria
Leeftijden die in aanmerking komen voor studie
- Volwassen
- Oudere volwassene
Accepteert gezonde vrijwilligers
Nee
Bemonsteringsmethode
Niet-waarschijnlijkheidssteekproef
Studie Bevolking
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.
Beschrijving
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.
Studie plan
Dit gedeelte bevat details van het studieplan, inclusief hoe de studie is opgezet en wat de studie meet.
Hoe is de studie opgezet?
Ontwerpdetails
Cohorten en interventies
Groep / Cohort |
Interventie / Behandeling |
|---|---|
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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.
|
Wat meet het onderzoek?
Primaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
|---|---|---|
|
Diagnostic performance of DeepBMD model for osteoporosis screening
Tijdsspanne: 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.
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Concurrent with the DXA validation visit following the CT analysis (within 7 days).
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Secundaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
|---|---|---|
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Feasibility of the DeepBMD screening and recall workflow
Tijdsspanne: At the end of recruitment
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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.
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At the end of recruitment
|
Medewerkers en onderzoekers
Hier vindt u mensen en organisaties die betrokken zijn bij dit onderzoek.
Onderzoekers
- Hoofdonderzoeker: Fan Yang, PhD, MD, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Publicaties en nuttige links
De persoon die verantwoordelijk is voor het invoeren van informatie over het onderzoek stelt deze publicaties vrijwillig ter beschikking. Dit kan gaan over alles wat met het onderzoek te maken heeft.
Algemene publicaties
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Studie record data
Deze datums volgen de voortgang van het onderzoeksdossier en de samenvatting van de ingediende resultaten bij ClinicalTrials.gov. Studieverslagen en gerapporteerde resultaten worden beoordeeld door de National Library of Medicine (NLM) om er zeker van te zijn dat ze voldoen aan specifieke kwaliteitscontrolenormen voordat ze op de openbare website worden geplaatst.
Bestudeer belangrijke data
Studie start (Werkelijk)
28 juli 2026
Primaire voltooiing (Werkelijk)
28 augustus 2026
Studie voltooiing (Geschat)
1 september 2026
Studieregistratiedata
Eerst ingediend
22 juli 2026
Eerst ingediend dat voldeed aan de QC-criteria
27 juli 2026
Eerst geplaatst (Werkelijk)
31 juli 2026
Updates van studierecords
Laatste update geplaatst (Werkelijk)
3 september 2026
Laatste update ingediend die voldeed aan QC-criteria
1 september 2026
Laatst geverifieerd
1 september 2026
Meer informatie
Termen gerelateerd aan deze studie
Trefwoorden
Aanvullende relevante MeSH-voorwaarden
Andere studie-ID-nummers
- UHCT260668
Plan Individuele Deelnemersgegevens (IPD)
Bent u van plan om gegevens van individuele deelnemers (IPD) te delen?
JA
Beschrijving IPD-plan
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-tijdsbestek voor delen
Data will be available beginning 3 months following article publication and ending 36 months following article publication.
IPD-toegangscriteria voor delen
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 delen Ondersteunend informatietype
- LEERPROTOCOOL
- SAP
- ANALYTIC_CODE
Informatie over medicijnen en apparaten, studiedocumenten
Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel
Nee
Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct
Nee
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