Deep Learning-based Coronary Stenosis Detection in X-ray Angiography Images: Overview and Future Trends

Ovalle-Magallanes, Emmanuel; Alvarado-Carrillo, Dora E.; Aviña-Cervantes, Juan Gabriel; Cruz-Aceves, Iván; Ruiz-Pinales, José; Correa, Rodrigo Amezcua

doi:10.1007/978-3-031-11170-9_8

Cited by 4 publications

(1 citation statement)

References 47 publications

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“…The need for extensive human interaction during image data and training label preparation, in addition to addressing problems of sampling imbalance during supervised-learning, has led to algorithms that are commonly evaluated on small datasets prone to overfitting ( 7 ). Clinically speaking, those studies generally aimed to differentiate significant stenosis from non-significant stenosis in CAG images while developing a tool to facilitate safety screening of a large volume of CAG images by separating cases with normal or mild stenoses from those with higher stenosis severities have not been targeted ( 24 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based end-to-end automated stenosis classification and localization on catheter coronary angiography

Chen

Kato

Vasconcellos

et al. 2023

Front. Cardiovasc. Med.

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BackgroundAutomatic 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.MethodsA 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.ResultsOur 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.ConclusionWe 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.

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Section: Introductionmentioning

confidence: 99%