RT Journal Article
T1 CADICA: A new dataset for coronary artery disease detectionby using invasive coronary angiograph
A1 Jiménez-Partinen, Ariadna
A1 Molina-Cabello, Miguel Ángel
A1 Thurnhofer-Hemsi, Karl
A1 Palomo-Ferrer, Esteban José
A1 Rodríguez Capitán, Jorge
A1 Molina Ramos, Ana Isabel
A1 Jiménez-Navarro, Manuel Francisco
K1 Sistema cardiovascular-Enfermedades
K1 Informática-Aplicaciones
K1 Diagnóstico por imagen
K1 Arterias coronarias-Enfermedades
AB 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.
PB Wiley
YR 2024
FD 2024
LK https://hdl.handle.net/10630/34542
UL https://hdl.handle.net/10630/34542
LA eng
NO 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
NO Funding for open access charge: Universidad de Málaga/CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026