Intelligent Detection of Carotid Plaque and Its Stability Based on Deep Learning Dynamic Ultrasound Scanning

This study intends to build a model through deep learning that can automatically and accurately detect plaques, calculate the lumen stenosis rate and evaluate the stability of plaques based on the carotid transverse axis dynamic ultrasound images and contrast-enhanced ultrasound images, so as to comprehensively evaluate the possibility of carotid plaques. cardiovascular risk. The successful development of this study will automatically simulate and reproduce the whole process of carotid plaque assessment by clinical sonographers. Solve the problem of ultrasonic inspection equipment and experience dependence. It is expected to carry out large-scale population intelligent screening, providing new ideas for early prevention and treatment. Especially in medically underdeveloped remote areas and the lack of experienced sonographers, it has great practical value in clinical health care and can bring greater social and economic benefits.