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OpenTrials
Active, Not Recruiting

NCT Number: NCT06658600

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

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Tsinghua University, Beijing, Beijing Municipality, China

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About this study

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:

  • Clinical Intuition (baseline assessment) Physicians assess obstructive CHD probability without any external assistance. Assessment relies solely on the physician's clinical judgment and experience.
  • 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.

  • 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.

Who can participate

Healthy volunteers accepted: No

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

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

Treatment and study plan

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

Other

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

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

Other

Physicians use a RF-CL table (risk factor weighted clinical likelihood table) to calculate the probability of obstructive CHD.

Primary outcomes

  1. 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

  1. Time Consumed by Physician Readers to Provide the Diagnosis Impression of Obstructive Coronary Heart Disease.

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

    The time consumed by physician readers will be recorded by an algorithm implemented on the website for reading.

Sponsors and collaborators

Lead sponsor

Tsinghua University

Other

Collaborators

  • Shanghai Health and Medical Center
  • Shanghai Jiao Tong University Affiliated Sixth People's Hospital

Registry information

Acronym: DeepCHD

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Oct 26, 2024
Registry last updated
Apr 8, 2025

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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