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

N/AActive Not RecruitingNCT07739628

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology100 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

100

Conditions

Osteoporosis

Interventions

DeepBMD model for osteoporosis risk screeningDIAGNOSTIC_TEST

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

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.

Time frame: Concurrent with the DXA validation visit following the CT analysis (within 7 days).

Secondary Outcomes

Feasibility of the DeepBMD screening and recall workflow

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.

Time frame: At the end of recruitment