Development and validation of deep learning model for detection of obstructive coronary artery disease in patients with acute chest pain: a multi-center study.

Abstract

Purpose: This study aimed to develop and validate a deep learning (DL) model to detect obstructive coronary artery disease (CAD, ≥ 50% stenosis) in coronary CT angiography (CCTA) among patients presenting to the emergency department (ED) with acute chest pain.

Materials and methods: The training dataset included 378 patients with acute chest pain who underwent CCTA (10,060 curved multiplanar reconstruction [MPR] images) from a single-center ED between January 2015 and December 2022. The external validation dataset included 298 patients from 3 ED centers between January 2021 and December 2022. A DL model based on You Only Look Once v4, requires manual preprocessing for curved MPR extraction and was developed using 15 manually preprocessed MPR images per major coronary artery. Model performance was evaluated per artery and per patient.

Results: The training dataset included 378 patients (mean age 61.3 ± 12.2 years, 58.2% men); the external dataset included 298 patients (mean age 58.3 ± 13.8 years, 54.6% men). Obstructive CAD prevalence in the external dataset was 27.5% (82/298). The DL model achieved per-artery sensitivity, specificity, positive predictive value, negative predictive value (NPV), and area under the curve (AUC) of 92.7%, 89.9%, 62.6%, 98.5%, and 0.919, respectively; and per-patient values of 93.3%, 80.7%, 67.7%, 96.6%, and 0.871, respectively.

Conclusions: The DL model demonstrated high sensitivity and NPV for identifying obstructive CAD in patients with acute chest pain undergoing CCTA, indicating its potential utility in aiding ED physicians in CAD detection.