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Automatic Stenosis Recognition from Coronary Angiography Using Convolutional Neural Networks by Jong Hak Moon; Da Young Lee; Won Chul Cha; Myung Jin Chung; Kyu-Sung Lee; Baek Hwan Cho; Jin Ho Choi is a Medicine article available to read on EtoBox.

What is Automatic Stenosis Recognition from Coronary Angiography Using Convolutional Neural Networks about?

## Background and objective: Coronary artery disease, which is mostly caused by atherosclerotic narrowing of the coronary artery lumen, is a leading cause of death. coronary angiography is the standard method to estimate the severity of coronary artery stenosis, but is frequently limited by intra- and inter-observer variations. we propose a deep-learning algorithm that automatically recognizes stenosis in coronary angiographic images. ## Methods: The proposed method consists of key frame detection, deep learning model training for classification of stenosis on each key frame, and visualization of the possible location of the stenosis. firstly, we propose an algorithm that automatically extracts key frames essential for diagnosis from 452 right coronary artery angiography movie clips. our deep learning model is then trained with image-level annotations to classify the areas narrowed by over 50 %. to make the model focus on the salient features, we apply a self-attention mechanism. the stenotic locations are visualized using the activated area of feature maps with gradient-weighted class activation mapping. ## Results: The automatically detected key frame was very close to the manual

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Author

Jong Hak Moon; Da Young Lee; Won Chul Cha; Myung Jin Chung; Kyu-Sung Lee; Baek Hwan Cho; Jin Ho Choi

Published

2020

Language

EN

Field

Medicine (Physical Sciences)