CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography

• URL: http://arxiv.org/abs/2402.00570v2
• Date: Fri, 16 Feb 2024 15:48:48 GMT
• Title: CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography
• Authors: Ariadna Jim\'enez-Partinen, Miguel A. Molina-Cabello, Karl Thurnhofer-Hemsi, Esteban J. Palomo, Jorge Rodr\'iguez-Capit\'an, Ana I. Molina-Ramos, Manuel Jim\'enez-Navarro
• Abstract summary: Coronary artery disease (CAD) remains the leading cause of death globally. Deep learning classification methods are well-developed in other areas of medical imaging. One of the most important reasons is the lack of available and high-quality open-access datasets.
• Score: 1.5404452377809545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Coronary artery disease (CAD) remains the leading cause of death globally and invasive coronary angiography (ICA) is considered the gold standard of anatomical imaging evaluation when CAD is suspected. However, risk evaluation based on ICA has several limitations, such as visual assessment of stenosis severity, which has significant interobserver variability. This motivates to development of a lesion classification system that can support specialists in their clinical procedures. Although deep learning classification methods are well-developed in other areas of medical imaging, ICA image classification is still at an early stage. One of the most important reasons is the lack of available and high-quality open-access datasets. In this paper, we reported a new annotated ICA images dataset, CADICA, to provide the research community with a comprehensive and rigorous dataset of coronary angiography consisting of a set of acquired patient videos and associated disease-related metadata. This dataset can be used by clinicians to train their skills in angiographic assessment of CAD severity and by computer scientists to create computer-aided diagnostic systems to help in such assessment. In addition, baseline classification methods are proposed and analyzed, validating the functionality of CADICA and giving the scientific community a starting point to improve CAD detection.

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