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oai:riuma.uma.es:10630/345422026-02-03T11:35:36Zcom_10630_2254col_10630_37953

CADICA: A new dataset for coronary artery disease detectionby using invasive coronary angiograph Jiménez-Partinen, Ariadna Molina-Cabello, Miguel Ángel Thurnhofer-Hemsi, Karl Palomo-Ferrer, Esteban José Rodríguez Capitán, Jorge Molina Ramos, Ana Isabel Jiménez-Navarro, Manuel Francisco Sistema cardiovascular-Enfermedades Informática-Aplicaciones Diagnóstico por imagen Arterias coronarias-Enfermedades 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, by computer scientists to create computer-aided diagnostic systems to help in such assessment, and to validate existing methods for CAD detection. In addition, baseline classification methods are proposed and analysed, validating the functionality of CADICA with deep learning-based methods and giving the scientific community a starting point to improve CAD detection. 2024-10-09T08:46:54Z 2024-10-09T08:46:54Z 2024-10-09T08:46:54Z 2024 journal article Jiménez-Partinen, A., Molina-Cabello, M. A., Thurnhofer-Hemsi, K., Palomo, E. J., Rodríguez-Capitán, J., Molina-Ramos, A. I., & Jiménez-Navarro, M. (2024). CADICA: A new dataset for coronary artery disease detection by using invasive coronary angiography. Expert Systems, e13708. https://doi.org/10.1111/exsy.13708 https://hdl.handle.net/10630/34542 https://doi.org/10.1111/exsy.13708 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Wiley