Osteoporosis
Conditions
Keywords
Osteoporosis, Opportunistic Screening, Deep Learning, Bone Mineral Density, Computed Tomography (CT)
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. 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.
Interventions
DIAGNOSTIC_TESTDeepBMD model for osteoporosis risk screening
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.
Sponsors
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance of DeepBMD model for osteoporosis screening | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of the DeepBMD screening and recall workflow | 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. |
Countries
China
Contacts
PRINCIPAL_INVESTIGATORFan Yang, PhD, MD
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Outcome results
None listed