Method and apparatus for identifying vulnerable coronary plaque based on multimodal large language model (mm-llm)

Abstract

A method and apparatus for identifying a vulnerable coronary plaque based on a multimodal large language model (MM-LLM) are provided. The method includes: acquiring a target 3D coronary computed tomography angiography (CCTA) image and a target straightened curved planar reformation (SCPR) image; acquiring plaque mask information of a blood vessel based on the target SCPR image; acquiring basic plaque data based on the plaque mask information; acquiring fluid dynamics data of each plaque area based on the SCPR image; acquiring basic clinical testing information; standardizing above image data, structural data, and textual data separately, and performing, by a regression mapping network, feature fusion to acquire a first fusion feature; acquiring a trained vulnerable plaque identification LLM; and inputting the first fusion feature into the vulnerable plaque identification LLM to acquire an identification result. The method solve problems of poor single-image feature expression ability and low identification performance in traditional technologies.