Histopathology-Based Deep-Learning Predicts Atherosclerotic Lesions in Intravascular Imaging.

ABSTRACT: Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT). Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were base

SUBMITTER: Holmberg O 

PROVIDER: S-EPMC8713728 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature