Coronary X-ray angiography segmentation using Artificial Intelligence: a multicentric validation study of a deep learning model.

ABSTRACT:

Introduction

We previously developed an artificial intelligence (AI) model for automatic coronary angiography (CAG) segmentation, using deep learning. To validate this approach, the model was applied to a new dataset and results are reported.

Methods

Retrospective selection of patients undergoing CAG and percutaneous coronary intervention or invasive physiology assessment over a one month period from four centers. A single frame was selected from images containing a lesion with a 50-99% stenosis (visual estimation). Automatic Quantitative Coronary Analysis (QCA) was performed with a validated software. Images were then segmented by the AI model. Lesion diameters, area overlap [based on true positive (TP) and true negative (TN) pixels] and a global segmentation score (GSS - 0

SUBMITTER: Nobre Menezes M 

PROVIDER: S-EPMC10250252 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature