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NCT Number: NCT06695273

Using Retinal Photograph Based AI to Predict Incident Coronary Heart Disease

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.

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Key information

Age range

40 year–75 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

AI-derived probability of coronary heart disease.

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.

PCEs derived ASCVD risk

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.

Primary outcomes

  1. Accuracy

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

HONGWEI JI

CONTACT

[email protected]

+8613120518791

Sponsors and collaborators

Lead sponsor

Tsinghua University

Other

Registry information

Acronym: DeepCHD Plus

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Nov 19, 2024
Registry last updated
Nov 19, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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