Deep learning-based prediction for significant coronary artery stenosis on coronary computed tomography angiography in asymptomatic populations

Heesun Lee; Bong Gyun Kang; Jeonghee Jo; Hyo Eun Park; Sungroh Yoon; Su-Yeon Choi; Min Joo Kim

doi:10.3389/fcvm.2023.1167468

Deep learning-based prediction for significant coronary artery stenosis on coronary computed tomography angiography in asymptomatic populations

Front Cardiovasc Med. 2023 Jun 21:10:1167468. doi: 10.3389/fcvm.2023.1167468. eCollection 2023.

Authors

Heesun Lee^#¹, Bong Gyun Kang², Jeonghee Jo^#³, Hyo Eun Park¹, Sungroh Yoon^{2

4}, Su-Yeon Choi¹, Min Joo Kim^#¹

Affiliations

¹ Department of Internal Medicine, School of Medicine, Seoul National University, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea.
² Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea.
³ Institute of New Media and Communications, Seoul National University, Seoul, Republic of Korea.
⁴ Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.

^# Contributed equally.

Abstract

Background: Although coronary computed tomography angiography (CCTA) is currently utilized as the frontline test to accurately diagnose coronary artery disease (CAD) in clinical practice, there are still debates regarding its use as a screening tool for the asymptomatic population. Using deep learning (DL), we sought to develop a prediction model for significant coronary artery stenosis on CCTA and identify the individuals who would benefit from undergoing CCTA among apparently healthy asymptomatic adults.

Methods: We retrospectively reviewed 11,180 individuals who underwent CCTA as part of routine health check-ups between 2012 and 2019. The main outcome was the presence of coronary artery stenosis of ≥70% on CCTA. We developed a prediction model using machine learning (ML), including DL. Its performance was compared with pretest probabilities, including the pooled cohort equation (PCE), CAD consortium, and updated Diamond-Forrester (UDF) scores.

Results: In the cohort of 11,180 apparently healthy asymptomatic individuals (mean age 56.1 years; men 69.8%), 516 (4.6%) presented with significant coronary artery stenosis on CCTA. Among the ML methods employed, a neural network with multi-task learning (19 selected features), one of the DL methods, was selected due to its superior performance, with an area under the curve (AUC) of 0.782 and a high diagnostic accuracy of 71.6%. Our DL-based model demonstrated a better prediction than the PCE (AUC, 0.719), CAD consortium score (AUC, 0.696), and UDF score (AUC, 0.705). Age, sex, HbA1c, and HDL cholesterol were highly ranked features. Personal education and monthly income levels were also included as important features of the model.

Conclusion: We successfully developed the neural network with multi-task learning for the detection of CCTA-derived stenosis of ≥70% in asymptomatic populations. Our findings suggest that this model may provide more precise indications for the use of CCTA as a screening tool to identify individuals at a higher risk, even in asymptomatic populations, in clinical practice.

Keywords: computed tomographic angiography; coronary artery disease; coronary stenosis; deep learning; diagnostic screening programs; neural networks.

Grants and funding

This study was supported by the SNUH Healthcare System Gangnam Center Research Fund (Grant No. 2020-02), Institute of Information & communications Technology Planning & Evaluation grant funded by the Korea government (Grant No. 2021-0-01343), and Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (Grant No. 2022R1A6A3A01087603).