The invention discloses a
coronary artery disease early risk prediction and
typing method based on
deep learning, and belongs to the technical field of medical
image processing. According to the method, cardiac
angiography CTA image pixel-level segmentation is completed by using an
image segmentation model, loss is optimized by using a medical image text
feature fusion module and multi-
modal feature alignment, and early risk prediction and
typing of coronary
artery diseases are completed. By introducing the multi-scale
state space feature
encoder, the hierarchical feature reconstruction module and the multi-
modal feature alignment optimization loss module, efficient and
accurate segmentation and
risk assessment of the coronary
artery CTA image are realized, a new technical means is provided for segmentation and
risk assessment of the coronary
artery CTA image, and the segmentation and
risk assessment efficiency of the coronary artery CTA image is improved. The development of a multi-
modal fusion technology in the field of medical
image processing is promoted, powerful support is provided for early diagnosis,
risk stratification and
personalized treatment of coronary
artery diseases, and the method has wide application prospects and important practical value.