Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease

NCT ID: NCT06695273

Last Updated: 2024-11-19

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1570 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-01-31
• Study Completion Date: 2025-05-31

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.

Conditions

Coronary Heart Disease (CHD)

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: SINGLE

Study Groups

AI-Assisted Group (AI Group)

Physicians receive CHD probability estimates from an AI model based on retinal photographs. The AI tool provides individualized CHD probabilities, leveraging retinal biomarkers associated with cardiovascular risk.

Group Type: EXPERIMENTAL

AI-derived probability of coronary heart disease.

Intervention Type: DIAGNOSTIC_TEST

Physician readers will be assisted with AI-derived probability of coronary heart disease. The AI tool provides individualized obstructive CHD probabilities and diagnosis, leveraging retinal biomarkers associated with cardiovascular risk.

Guideline-Based Group (Guideline Group)

Physicians use a PCE calculator to calculate the 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making.

Group Type: ACTIVE_COMPARATOR

PCEs derived ASCVD risk

Intervention Type: DIAGNOSTIC_TEST

Physicians use a PCEs to calculate the probability of 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making.

Interventions

AI-derived probability of coronary heart disease.

Physician readers will be assisted with AI-derived probability of coronary heart disease. The AI tool provides individualized obstructive CHD probabilities and diagnosis, leveraging retinal biomarkers associated with cardiovascular risk.

Intervention Type: DIAGNOSTIC_TEST

PCEs derived ASCVD risk

Physicians use a PCEs to calculate the probability of 10 year ASCVD risk. This approach aligns with current clinical guidelines to assist in decision-making.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Individuals without uncontrolled vascular risk factors
• Age range: 40-75 years old
• Can accept and cooperate with the examination and potential follow-up work after being selected for clinical trials

Exclusion Criteria

• Severe lung disease and cancer or surgery patients
• Statin user or pre-existing cardiovascular disease
• Individuals with severe liver and kidney dysfunction and electrolyte imbalance

• Minimum Eligible Age: 40 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Tsinghua University

OTHER

Sponsor Role: lead

Responsible Party

Tien Yin Wong

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Tien Yin Wong

Role: PRINCIPAL_INVESTIGATOR

Tsinghua University

Central Contacts

HONGWEI JI

Role: CONTACT

hongweijicn@gmail.com

+8613120518791

Other Identifiers

DeepCHD Plus

Identifier Type: -

Identifier Source: org_study_id