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

Tsinghua University

Status: Active, not recruiting

Conditions: Coronary Heart Disease

Treatments: Other: Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease; Other: Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart disease

Study type: Interventional

Funder types: Other

Identifiers

NCT06658600

DeepCHD

Details and patient eligibility

About

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.

Full 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.

Enrollment: 900 estimated patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Individuals with symptoms of coronary heart disease
• Age range: 18-75 years old
• Can accept and cooperate with the examination and potential follow-up work after being selected for clinical trials

Exclusion criteria

• Severe hypertension (>180/110mmHg)
• Complex arrhythmia (atrial fibrillation, atrial flutter, frequent premature beats)
• Severe lung disease and chest malformation or surgery patients
• Acute myocardial infarction occurring less than 3 months ago
• Individuals with severe liver and kidney dysfunction and electrolyte imbalance

Trial design

Primary purpose: Screening

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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900 participants in 2 patient groups

Guideline-Based Group (Guideline Group)

Active Comparator group

Description:

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.

Treatment:

Other: Physician readers will be assisted with RF-CL table to calculate the probability of obstructive coronary heart disease

AI-Assisted Group (AI Group)

Experimental group

Description:

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.

Treatment:

Other: Physician readers will be assisted with AI-derived probability and diagnosis of obstructive coronary heart disease