TY - JOUR
T1 - Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography
AU - Cong, Chao
AU - Kato, Yoko
AU - Vasconcellos, Henrique Doria De
AU - Ostovaneh, Mohammad R.
AU - Lima, Joao A.C.
AU - Ambale-Venkatesh, Bharath
N1 - Funding Information:
This study was supported by Master Research Agreement 09–115 and Artificial Intelligence Health Information Exchange (AIHEX).
Funding Information:
JL reported receipt of grant support from Canon Medical Systems. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher Copyright:
Copyright © 2023 Cong, Kato, Vasconcellos, Ostovaneh, Lima and Ambale-Venkatesh.
PY - 2023/2/7
Y1 - 2023/2/7
N2 - Background: Automatic coronary angiography (CAG) assessment may help in faster screening and diagnosis of stenosis in patients with atherosclerotic disease. We aimed to provide an end-to-end workflow that separates cases with normal or mild stenoses from those with higher stenosis severities to facilitate safety screening of a large volume of the CAG images. Methods: A deep learning-based end-to-end workflow was employed as follows: (1) Candidate frame selection from CAG videograms with Convolutional Neural Network (CNN) + Long Short Term Memory (LSTM) network, (2) Stenosis classification with Inception-v3 using 2 or 3 categories (<25%, >25%, and/or total occlusion) with and without redundancy training, and (3) Stenosis localization with two methods of class activation map (CAM) and anchor-based feature pyramid network (FPN). Overall 13,744 frames from 230 studies were used for the stenosis classification training and fourfold cross-validation for image-, artery-, and per-patient-level. For the stenosis localization training and fourfold cross-validation, 690 images with > 25% stenosis were used. Results: Our model achieved an accuracy of 0.85, sensitivity of 0.96, and AUC of 0.86 in per-patient level stenosis classification. Redundancy training was effective to improve classification performance. Stenosis position localization was adequate with better quantitative results in anchor-based FPN model, achieving global-sensitivity for left coronary artery (LCA) and right coronary artery (RCA) of 0.68 and 0.70. Conclusion: We demonstrated a fully automatic end-to-end deep learning-based workflow that eliminates the vessel extraction and segmentation step in coronary artery stenosis classification and localization on CAG images. This tool may be useful to facilitate safety screening in high-volume centers and in clinical trial settings.
AB - Background: Automatic coronary angiography (CAG) assessment may help in faster screening and diagnosis of stenosis in patients with atherosclerotic disease. We aimed to provide an end-to-end workflow that separates cases with normal or mild stenoses from those with higher stenosis severities to facilitate safety screening of a large volume of the CAG images. Methods: A deep learning-based end-to-end workflow was employed as follows: (1) Candidate frame selection from CAG videograms with Convolutional Neural Network (CNN) + Long Short Term Memory (LSTM) network, (2) Stenosis classification with Inception-v3 using 2 or 3 categories (<25%, >25%, and/or total occlusion) with and without redundancy training, and (3) Stenosis localization with two methods of class activation map (CAM) and anchor-based feature pyramid network (FPN). Overall 13,744 frames from 230 studies were used for the stenosis classification training and fourfold cross-validation for image-, artery-, and per-patient-level. For the stenosis localization training and fourfold cross-validation, 690 images with > 25% stenosis were used. Results: Our model achieved an accuracy of 0.85, sensitivity of 0.96, and AUC of 0.86 in per-patient level stenosis classification. Redundancy training was effective to improve classification performance. Stenosis position localization was adequate with better quantitative results in anchor-based FPN model, achieving global-sensitivity for left coronary artery (LCA) and right coronary artery (RCA) of 0.68 and 0.70. Conclusion: We demonstrated a fully automatic end-to-end deep learning-based workflow that eliminates the vessel extraction and segmentation step in coronary artery stenosis classification and localization on CAG images. This tool may be useful to facilitate safety screening in high-volume centers and in clinical trial settings.
KW - catheter coronary angiography
KW - deep learning
KW - end-to-end workflow
KW - redundancy training
KW - stenosis classification
KW - stenosis localization
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U2 - 10.3389/fcvm.2023.944135
DO - 10.3389/fcvm.2023.944135
M3 - Article
C2 - 36824452
AN - SCOPUS:85148534343
SN - 2297-055X
VL - 10
JO - Frontiers in Cardiovascular Medicine
JF - Frontiers in Cardiovascular Medicine
M1 - 944135
ER -