Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease

Not Applicable

Not yet recruiting

• Conditions: Coronary Heart Disease (CHD)

• Registration Number: NCT06695273
• Lead Sponsor: Tsinghua University

Brief Summary

To determine whether an integrated retinal AI decision support can improve predictive accuracy of coronary heart disease (CHD), the investigators are conducting a randomized controlled study of AI guided prediction of CHD compared to clinical prediction by physicians (e.g., usingPCEs), both using clinical intuition as baseline.

Detailed Description

This is a randomized controlled trial (RCT) evaluating the effectiveness of an AI-based decision support tool in CHD risk prediction and decision making by physicians. Prospective cohort study participant cases will be randomly assigned to either guideline group (e.g., PCEs) or AI group after baseline assessment (clinical intuition):

There are three settings: (1) Clinical Intuition (baseline assessment) Physicians' make decision about prevention strategy initiation (e.g., statin initiation) without any external assistance. Assessment relies solely on the physician's clinical judgment and experience. (2) Guideline-Based Group (Guideline Group) Physicians use a PCE table to calculate the 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making. (3) AI-Assisted Group (AI Group) Physicians receive CHD probability estimates 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 CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. The accuracy could be assessed by the extent of prevention initiation (e.g., prescribing statins) corresponding with actual CHD outcomes observed.

Secondary Objective To assess whether AI-guided decision support reduces the time required to complete CHD assessments and decision making.

Participants, Readers and Randomization:

Participants: Participants in prospective cohort studies, with 10-year follow up.

Readers: Physicians performing evaluations of CHD probability and make primary prevention recommendations.

Randomization: Participants will be randomized into one of the groups (PCEs or AI) after clinical assessment at baseline using block randomization to ensure balanced group sizes.

Study Timeline

• Status Verified: 2024-11
• Study First Submitted: 2024-11-16
• Study First Submitted QC: 2024-11-16
• Last Update Submitted: 2024-11-16
• Study First Posted: 2024-11-19
• Last Update Posted: 2024-11-19
• Study Start: 2025-01
• Primary Completion: 2025-04
• Study Completion: 2025-05

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 1570

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Accuracy	Through study completion, an average of 1 week	To evaluate whether AI-guided decision support could improves diagnostic accuracy of CHD to a greater extent than standard clinical assessments, both compared to clinical intuition. The accuracy could be assessed by the degree to which prevention initiation (e.g., prescribing statins) align with actual CHD outcomes observed.