Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods
Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods
The incidence rate and mortality of coronary artery disease are increasing year by year. Exploring non-invasive, accurate, and widely applicable methods to screen and diagnosis is of great significance. New ultrasound techniques, such as non-invasive myocardial work, have been proven to be superior to traditional ultrasound techniques in screening and diagnosis. However, diagnostic analysis based on ultrasound video images is time-consuming and subjective. The progress of artificial intelligence technology in fully automated quantitative evaluation of video images provides the possibility for computer-aided design screening and diagnosis. At present, the application of artificial intelligence in computer-aided design is a cutting-edge issue in the field of cardiovascular disease research. The application of artificial intelligence technology in the construction of computer-aided diagnostic models based on ultrasound video images is still in its early stages.
1) Clarify the value of new cardiac ultrasound techniques indicators in coronary artery disease diagnosis; 2) To achieve classification and detection of cardiac ultrasound sections; Implementing automatic segmentation and recognition of the left ventricular cavity, left ventricular myocardium, and left atrial wall contours through the CLAS model; Using the another model to achieve heart motion tracking and synthesizing velocity vector maps of the heart flow field. 3) Verify and optimize the coronary artery disease fully automated artificial intelligence diagnostic model mentioned above.
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