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

Jin Young Kim¹, Jiyong Park², Kye Ho Lee³, Ji Won Lee⁴, Jinho Park², Pan Ki Kim², Kyunghwa Han⁵, Song-Ee Baek⁵, Dong Jin Im⁵, Byoung Wook Choi⁵, Jin Hur⁶

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

²Department of Research and Development, Phantomics Inc., Seoul, South Korea.

³Department of Radiology, Dankook University Hospital, Cheonan, Chungnam Province, Republic of Korea.

⁴Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan, Korea.

⁵Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea.

⁶Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea. Khuhz@yuhs.ac.

PubMed DOI

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 reco…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Mentioned Companies

Phantomics
affiliation

Other Funding

Korea Evaluation Institute of Industrial Technology(P0021355)

MeSH Terms

Humans0Deep Learning*Male0Female0Middle Aged0Chest Pain*Computed Tomography Angiography*Coronary Angiography*Coronary Artery Disease*Aged0Sensitivity and Specificity0Emergency Service, Hospital0Retrospective Studies0Acute Pain*Acute Disease0

Keywords

Artificial intelligenceCoronary CT angiographyCoronary artery diseaseDeep learningEmergency department

Article Info

PMID: 40810889
DOI: 10.1007/s11547-025-02064-1
Received: July 9, 2024
Accepted: July 16, 2025

How to Cite

Kim JY et al. Development and validation of deep learning model for detection of obstructive coronary artery disease in patients with acute chest pain: a multi-center study. Radiol Med2025;130(10):1615-1624. doi:10.1007/s11547-025-02064-1

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.