TY - JOUR

T1 - Classification of Left and Right Coronary Arteries in Coronary Angiographies Using Deep Learning

AU - Eschen, Christian Kim

AU - Banasik, Karina

AU - Christensen, Alex Hørby

AU - Chmura, Piotr Jaroslaw

AU - Pedersen, Frants

AU - Køber, Lars

AU - Engstrøm, Thomas

AU - Dahl, Anders Bjorholm

AU - Brunak, Søren

AU - Bundgaard, Henning

N1 - Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2022

Y1 - 2022

N2 - Multi-frame X-ray images (videos) of the coronary arteries obtained using coronary angiography (CAG) provide detailed information about the anatomy and blood flow in the coronary arteries and play a pivotal role in diagnosing and treating ischemic heart disease. Deep learning has the potential to quickly and accurately quantify narrowings and blockages of the arteries from CAG videos. A CAG consists of videos acquired separately for the left coronary artery and the right coronary artery (LCA and RCA, respectively). The pathology for LCA and RCA is typically only reported for the entire CAG, and not for the individual videos. However, training of stenosis quantification models is difficult when the RCA and LCA information of the videos are unknown. Here, we present a deep learning-based approach for classifying LCA and RCA in CAG videos. Our approach enables linkage of videos with the reported pathological findings. We manually labeled 3545 and 520 videos (approximately seven videos per CAG) to enable training and testing of the models, respectively. We obtained F1 scores of 0.99 on the test set for LCA and RCA classification LCA and RCA classification on the test set. The classification performance was further investigated with extensive experiments across different model architectures (R(2+1)D, X3D, and MVIT), model input sizes, data augmentations, and the number of videos used for training. Our results showed that CAG videos could be accurately curated using deep learning, which is an essential preprocessing step for a downstream application in diagnostics of coronary artery disease.

AB - Multi-frame X-ray images (videos) of the coronary arteries obtained using coronary angiography (CAG) provide detailed information about the anatomy and blood flow in the coronary arteries and play a pivotal role in diagnosing and treating ischemic heart disease. Deep learning has the potential to quickly and accurately quantify narrowings and blockages of the arteries from CAG videos. A CAG consists of videos acquired separately for the left coronary artery and the right coronary artery (LCA and RCA, respectively). The pathology for LCA and RCA is typically only reported for the entire CAG, and not for the individual videos. However, training of stenosis quantification models is difficult when the RCA and LCA information of the videos are unknown. Here, we present a deep learning-based approach for classifying LCA and RCA in CAG videos. Our approach enables linkage of videos with the reported pathological findings. We manually labeled 3545 and 520 videos (approximately seven videos per CAG) to enable training and testing of the models, respectively. We obtained F1 scores of 0.99 on the test set for LCA and RCA classification LCA and RCA classification on the test set. The classification performance was further investigated with extensive experiments across different model architectures (R(2+1)D, X3D, and MVIT), model input sizes, data augmentations, and the number of videos used for training. Our results showed that CAG videos could be accurately curated using deep learning, which is an essential preprocessing step for a downstream application in diagnostics of coronary artery disease.

KW - cardiology

KW - coronary angiography

KW - deep learning

KW - ischemic heart disease

KW - video classification

U2 - 10.3390/electronics11132087

DO - 10.3390/electronics11132087

M3 - Journal article

AN - SCOPUS:85133175710

VL - 11

JO - Electronics (Switzerland)

JF - Electronics (Switzerland)

SN - 2079-9292

IS - 13

M1 - 2087

ER -