Deep Learning Algorithm for Detecting Obstructive Coronary Artery Disease Using Fundus Photographs
Study Details
Study Description
Brief Summary
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| coronary artery disease group / non- coronary artery disease group 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)
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
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Outcome Measures
Primary Outcome Measures
- AUC [December 30, 2024]
To evaluate the algorithm performance area under the receiver operating characteristic curve (AUC) were calculated
Secondary Outcome Measures
- sensitivity [December 30, 2024]
To evaluate the algorithm performance, the sensitivity were calculated
- specificity [December 30, 2024]
To evaluate the algorithm performance, the specificity were calculated
Eligibility Criteria
Criteria
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.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Yong Zeng | Beijing | 北京 | China | 100029 |
Sponsors and Collaborators
- Yong Zeng
Investigators
- Principal Investigator: Yong Zeng, Beijing An Zhen Hospital: Capital Medical University Affiliated Anzhen Hospital
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- 121100004006885458