A deep learning methodology for fully-automated quantification of calcific burden in high-resolution intravascular ultrasound images.

ABSTRACT: Quantification of the calcific burden is valuable in percutaneous coronary intervention (PCI) planning and in research to assess its changes after pharmacotherapies targeting plaque progression. In intravascular ultrasound (IVUS) images this analysis is currently performed manually and time consuming. To overcome these limitations, we introduce a deep-learning (DL) method for seamless detection of the calcific tissue. IVUS images from 197 vessels were analysed by an expert who identified the presence of calcium, and these estimations were used to train a DL model for fast detection of calcific deposits. The output of the model was tested in a set of 30 vessels against the estimations of the two experts. Comparison was performed at a frame-, lesion- and segment level. In total 26,211 frames

SUBMITTER: He X 

PROVIDER: S-EPMC12847118 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature