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Published on: September 22, 2023
Automatic diagnosis of coronary artery stenosis by deep learning based on X-ray coronary angiography
Chengyu Mao1, Huasu Zeng1, Kandi Zhang1
1Department of Cardiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Insights
A new deep learning model automatically segments and grades coronary artery stenosis from X-ray angiography. This AI tool aids in diagnosing coronary artery disease (CAD) and planning patient treatments.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary artery disease (CAD) is a major global cause of death, often linked to plaque buildup in arteries.
- X-ray coronary angiography is standard for diagnosing CAD, but image interpretation for stenosis severity is challenging.
- Existing methods struggle with consistent interpretation of coronary artery stenosis severity from angiographic images.
Purpose of the Study:
- To develop and validate a deep learning (DL) approach for automated segmentation and grading of coronary artery stenosis.
- To improve the accuracy and consistency of stenosis assessment in X-ray coronary angiography.
- To provide a tool that aids in the diagnosis of coronary artery disease (CAD) and patient treatment planning.
Main Methods:
- A dual-output deep convolutional neural network (CNN) was developed using 383 angiographic images from 168 patients.
- Stenosis severity was manually annotated into five clinical levels: nonobstructive (1-49%), intermediate (50-70%), severe (71-95%), sub-total occlusion (96-99%), and total occlusion (100%).
- The DL model performed automated segmentation and grading of coronary artery stenosis.
Main Results:
- The developed model achieved high performance metrics for coronary stenosis segmentation and grading.
- Key performance indicators included an average Intersection over Union (IoU) of 0.92, Dice score of 0.95, precision of 0.93, and sensitivity of 0.96.
- The model demonstrated strong capabilities in identifying clinically significant stenosis grades (71-100%).
Conclusions:
- An image-based vascular analysis method using DL for X-ray coronary angiography was successfully developed.
- The method accurately localizes and grades coronary artery stenosis, particularly critical levels.
- This DL approach shows potential to enhance CAD diagnosis and personalized treatment strategies.
Background:
Coronary artery disease (CAD) is a leading cause of global mortality, primarily due to the accumulation of atheromatous plaques in coronary arteries. The current diagnostic standards include X-ray coronary angiography, which evaluates morphological features of the coronary arteries. The primary goal of this modality is to quantify stenosis severity, but variability and complexities in the resultant images make consistent interpretation challenging. This study developed a deep learning-based approach for segmenting and grading vessel stenosis via X-ray coronary angiography.
Methods:
Based on 383 angiographic images from 168 patients, we developed a dual-output deep convolutional neural network (CNN) to automatically diagnose stenosis. For clinical relevance, we manually annotated stenosis severity into five distinct levels: nonobstructive lesion (1-49%), intermediate lesion (50-70%), severe lesion (71-95%), sub-total occlusion (96-99%), and total occlusion (100%).
Results:
We built a coronary stenosis segmentation and grading method based on X-ray coronary angiography. The model achieved an average intersection over union (IoU) of 0.92, a Dice score of 0.95, a precision of 0.93, and a sensitivity of 0.96.
Conclusions:
We introduce an image-based vascular analysis method that localizes and grades stenosis in X-ray coronary angiography. This method can automatically identify clinically critical grades, especially within the 71-100% range. Deep learning methods can potentially facilitate the diagnosis of CAD and patient-centric treatment planning.
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