Related Experiment Video

Updated: Sep 11, 2025

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

525

Development and validation of deep learning model for detection of obstructive coronary artery disease in patients

Jin Young Kim1, Jiyong Park2, Kye Ho Lee3

  • 1Department of Radiology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Republic of Korea.

La Radiologia Medica
|August 14, 2025
PubMed

Insights

A deep learning model effectively detects obstructive coronary artery disease (CAD) in emergency department patients with acute chest pain using coronary CT angiography. This AI tool shows high accuracy, aiding physicians in diagnosing CAD.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Acute chest pain is a common emergency department presentation.
  • Coronary artery disease (CAD) is a leading cause of chest pain.
  • Early and accurate CAD detection is crucial for patient outcomes.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for detecting obstructive CAD (≥50% stenosis).
  • To assess the model's performance in patients presenting to the emergency department (ED) with acute chest pain using coronary CT angiography (CCTA).

Main Methods:

  • A DL model based on You Only Look Once v4 was developed using CCTA data from 378 ED patients.
  • The model utilized manually preprocessed curved multiplanar reconstruction (MPR) images of coronary arteries.
  • External validation was performed on a separate dataset of 298 patients from 3 ED centers.

Main Results:

  • The DL model achieved high per-artery sensitivity (92.7%) and negative predictive value (NPV, 98.5%).
  • Per-patient performance included a sensitivity of 93.3% and NPV of 96.6%.
  • The model demonstrated strong diagnostic capability with an area under the curve (AUC) of 0.919 for per-artery analysis.

Conclusions:

  • The DL model shows significant potential for aiding emergency physicians in detecting obstructive CAD in acute chest pain patients.
  • The model's high sensitivity and NPV suggest its utility in ruling out significant coronary artery disease.
  • Further integration into clinical workflows could improve diagnostic efficiency and patient care.
Abstract

Keywords:
Artificial intelligenceCoronary CT angiographyCoronary artery diseaseDeep learningEmergency department

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