Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs

Study Overview
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Brief Summary
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Artificial Intelligence, trained through model learning, can quickly perform medical image recognition and is widely used in early disease screening and assisted diagnosis. With the continuous optimization of deep learning, the application of AI has helped to discover some previously unknown associations with other systemic diseases. Artificial intelligence based on retinal fundus images can be used to detect anemia, hepatobiliary diseases, and chronic kidney disease, and to predict other systemic biomarkers. The above studies provide a theoretical basis for the application of artificial intelligence technology based on retinal fundus images to the diagnosis and prediction of cardiovascular diseases. At present, there is still a lack of accurate, rapid, and easy-to-use diagnostic and therapeutic tools for predictive modeling of coronary heart disease risk and early screening tools in China and the world. Fundus image is gradually used as a tool for extensive screening of diseases due to its special connection with blood vessels throughout the body, as well as easy access, cheap and efficient. It is of great scientific and social significance to develop and validate a model for identification and prediction of coronary heart disease and its risk factors based on fundus images using AI deep learning algorithms, and to explore the value of AI fundus images in assisting coronary heart disease diagnosis and screening for a wide range of applications.

Official Title
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Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs

Conditions
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Coronary Artery Disease, Artificial Heart Device User

Intervention / Treatment
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* Diagnostic Test: coronary artery imaging (coronary CTA or coronary angiography)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography. Exclusion Criteria: The exclusion criteria were as follows: (i) prior percutaneous coronary intervention (PCI); (ii) prior coronary artery bypass graft (CABG); (iii) other heart disease (e.g., congenital heart disease, valvular heart disease, or macrovascular disease); (iv) inability to have photographs taken; and (v) and a diagnosis of ST-segment elevation myocardial infarction (STEMI). Prior to the coronary angiography procedure, all eligible patients provided informed consent to participate in the study and to have their photographs used for research purposes.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| coronary artery disease group / non- coronary artery disease group<br>Recruited patients were categorized into a coronary artery disease group and a non-coronary artery disease group on the basis of coronary angiography findings, and the presence of CAD was defined as the presence of a coronary artery lesion with a stenosis | Diagnostic Test: coronary artery imaging (coronary CTA or coronary angiography)<br>* In order to obtain the gold standard labeling for coronary heart disease, this topic will form a panel of experts on labeling, and the diagnosis will be based on coronary angiography, defined as a lesion with a stenosis of at least 50% in at least one coronary artery<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| AUC | To evaluate the algorithm performance area under the receiver operating characteristic curve (AUC) were calculated | December 30, 2024 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| sensitivity | To evaluate the algorithm performance, the sensitivity were calculated | December 30, 2024 | 
| specificity | To evaluate the algorithm performance, the specificity were calculated | December 30, 2024 |