Artificial Heart Device User, Coronary Artery Disease
Conditions
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.
Interventions
DIAGNOSTIC_TESTcoronary 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
Sponsors
Yong Zeng
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
Eligible participants were ≥ 18 years of age, with clinically suspected CAD, and were scheduled for coronary angiography.
Exclusion criteria
The
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC | December 30, 2024 | To evaluate the algorithm performance area under the receiver operating characteristic curve (AUC) were calculated |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Countries
China
Contacts
Primary Contactyong zeng, Dr
Backup Contactyong zeng
Outcome results
None listed