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X-ray Coronary Angiogram images and SYNTAX score to develop Machine-Learning algorithms for CHD Diagnosis
Seyed Sajjad Mahmoudi1, Mohammad Matin Alishani2, Manijeh Emdadi3
1Department of Cardiology, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
Insights
This study introduces a new dataset of 231 coronary angiography images to advance Artificial Intelligence (AI) research for diagnosing Coronary Heart Disease (CHD). This resource aims to improve automated stenosis estimation and CHD diagnosis.
Area of Science:
- Cardiology and Medical Imaging
- Artificial Intelligence in Healthcare
Background:
- Coronary Heart Disease (CHD) is a major global health concern.
- Coronary angiography is the gold standard for assessing coronary artery stenosis but suffers from limitations like operator bias and variability.
- Automated interpretation of angiograms is needed but requires large, annotated datasets.
Purpose of the Study:
- To present a novel dataset of X-ray coronary angiography images for AI-driven research.
- To facilitate the development of machine learning algorithms for CHD diagnosis and stenosis estimation.
- To address the need for reproducible and objective analysis in interventional cardiology.
Main Methods:
- Collection of 231 X-ray heart vessel images.
- Inclusion of essential angiographic variables, such as the SYNTAX score.
- Dataset curated to support machine learning and data mining research in CHD.
Main Results:
- A dataset of 231 annotated coronary angiography images is now available.
- The dataset includes clinical and angiographic variables crucial for AI model training.
- This resource supports research into automated stenosis quantification.
Conclusions:
- The developed dataset is a valuable resource for advancing AI in CHD diagnosis.
- It aims to overcome limitations of manual angiogram interpretation.
- Facilitates future research in automated CHD assessment and treatment planning.
Abstract:
Coronary Heart Disease (CHD) is becoming a leading cause of death worldwide. To assess coronary artery narrowing or stenosis, doctors use coronary angiography, which is considered the gold-standard method. Interventional cardiologists rely on angiography to decide on the best course of treatment for CHD, such as revascularization with bypass surgery, coronary stents, or medication. However, angiography has some issues, including operator bias, inter-observer variability, and poor reproducibility. The automated interpretation of coronary angiography is yet to be developed, and these tasks can only be performed by highly specialized physicians. Developing automated angiogram interpretation and coronary artery stenosis estimation using Artificial Intelligence (AI) approaches requires a large dataset of X-ray angiography images that include clinical information. We have collected 231 X-ray images of heart vessels, along with the necessary angiographic variables, including the SYNTAX score, to support the advancement of research on CHD-related machine learning and data mining algorithms. We hope that this dataset will ultimately contribute to advances in clinical diagnosis of CHD.
