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Clinical Trials/NCT06658600
NCT06658600
Active, not recruiting
Not Applicable

Performance Evaluation of Artificial Intelligence Screening Model in Coronary Heart Disease Detection

Tsinghua University3 sites in 1 country900 target enrollmentJanuary 10, 2025

Overview

Phase
Not Applicable
Intervention
Not specified
Conditions
Coronary Heart Disease
Sponsor
Tsinghua University
Enrollment
900
Locations
3
Primary Endpoint
Diagnostic Accuracy of Participants with Obstructive Coronary Heart Disease
Status
Active, not recruiting
Last Updated
last year

Overview

Brief Summary

To determine whether an integrated AI decision support can save time and improve accuracy of assessment of obstructive coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided measurements of obstructive CHD probability compared to clinical assessment in preliminary evaluations by physicians.

Detailed Description

This is a randomized controlled trial (RCT) evaluating the effectiveness of an AI-based decision support tool in the preliminary assessment of obstructive CHD by physicians. Retrospectively collected medical records of participants with chest pain or dyspnea will be randomly assigned to either guideline group or AI group after baseline assessment: There are three settings: 1. Clinical Intuition (baseline assessment) Physicians assess obstructive CHD probability without any external assistance. Assessment relies solely on the physician's clinical judgment and experience. 2. Guideline-Based Group (Guideline Group) Physicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD. This approach aligns with current clinical guidelines to assist in decision-making. 3. AI-Assisted Group (AI Group) Physicians receive CHD probability estimates and diagnostic recommendations from an AI model based on retinal photographs. The AI tool provides individualized obstructive CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk. Primary Objective To evaluate whether AI-guided decision support could improves diagnostic accuracy of obstructive CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. Secondary Objective To assess whether AI-guided decision support reduces the time required to complete preliminary assessments of obstructive CHD. Participants, Readers and Randomization Participants: Case records of participants with chest pain or dyspnea, all underwent CT coronary angiography or invasive coronary angiography. Readers: Physicians performing preliminary evaluations of obstructive CHD patients. Randomization: Participants and readers will be randomized into one of the groups (RF-CL or AI) after clinical assessment at baseline using block randomization to ensure balanced group sizes.

Registry
clinicaltrials.gov
Start Date
January 10, 2025
End Date
May 2025
Last Updated
last year
Study Type
Interventional
Study Design
Parallel
Sex
All

Investigators

Responsible Party
Principal Investigator
Principal Investigator

Tien Yin Wong

Professor

Tsinghua University

Eligibility Criteria

Inclusion Criteria

  • Not provided

Exclusion Criteria

  • Not provided

Outcomes

Primary Outcomes

Diagnostic Accuracy of Participants with Obstructive Coronary Heart Disease

Time Frame: Through study completion, an average of 1 week

Whether AI-guided decision support improves the diagnostic accuracy of obstructive coronary heart disease (CHD) to a greater extent than standard clinical assessments (RF-CL), both compared to clinical intuition. All participants of the case records had underwent CT angiography or invasive angiography. The diagnostic accuracy, sensitivity and specificity will be compared across groups.

Secondary Outcomes

  • Time Consumed by Physician Readers to Provide the Diagnosis Impression of Obstructive Coronary Heart Disease.(Through study completion, an average of 1 week)

Study Sites (3)

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