X-ray Coronary Angiogram images and SYNTAX score to develop Machine-Learning algorithms for 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.