A dual-task deep learning framework for automated detection and classification of coronary artery lesions in invasive coronary angiography imaging.

ABSTRACT:

Objective

This study aims to develop and evaluate a dual-task deep learning framework for the simultaneous detection and classification of coronary lesions in invasive coronary angiography (ICA).

Materials and methods

A retrospective analysis was conducted using an annotated ICA dataset comprising 1234 patients (14,808 lesion-positive and 11,872 lesion-negative images), along with an external dataset of 135 cases. ICA video sequences were converted into 512 × 512 resolution images. A comprehensive preprocessing pipeline, including intensity normalization, noise suppression, and diverse data augmentation techniques (e.g., rotation, flipping, scaling, and brightness/contrast tuning), was applied to standardize image quality and enhance model generalizability. The proposed fram

SUBMITTER: Wa SRG 

PROVIDER: S-EPMC12930670 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature