Novel deep learning method for coronary artery tortuosity detection through coronary angiography
Autor
Cobo, Miriam
Pérez-Rojas, Francisco
Gutiérrez-Rodríguez, Constanza
Heredia, Ignacio
Maragaño-Lizama, Patricio
Yung-Manriquez, Francisca
Lloret Iglesias, Lara
Vega, José A.
Resumen
Coronary artery tortuosity is usually an undetected condition in patients undergoing coronary angiography. This condition requires a longer examination by the specialist to be detected. Yet, detailed knowledge of the morphology of coronary arteries is essential for planning any interventional treatment, such as stenting. We aimed to analyze coronary artery tortuosity in coronary angiography with artificial intelligence techniques to develop an algorithm capable of automatically detecting this condition in patients. This work uses deep learning techniques, in particular, convolutional neural networks, to classify patients into tortuous or non-tortuous based on their coronary angiography. The developed model was trained both on left (Spider) and right (45°/0°) coronary angiographies following a fivefold cross-validation procedure. A total of 658 coronary angiographies were included. Experimental results demonstrated satisfactory performance of our image-based tortuosity detection system, with a test accuracy of (87 ± 6)%. The deep learning model had a mean area under the curve of 0.96 ± 0.03 over the test sets. The sensitivity, specificity, positive predictive values, and negative predictive values of the model for detecting coronary artery tortuosity were (87 ± 10)%, (88 ± 10)%, (89 ± 8)%, and (88 ± 9)%, respectively. Deep learning convolutional neural networks were found to have comparable sensitivity and specificity with independent experts’ radiological visual examination for detecting coronary artery tortuosity for a conservative threshold of 0.5. These findings have promising applications in the field of cardiology and medical imaging. © 2023, The Author(s).
Colecciones
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Other
Publisher Correction: Novel deep learning method for coronary artery tortuosity detection through coronary angiography (Scientific Reports, (2023), 13, 1, (11137), 10.1038/s41598-023-37868-6) (2024)
Cobo, Miriam; Pérez-Rojas, Francisco; Gutiérrez-Rodríguez, Constanza; Heredia, Ignacio; Maragaño-Lizama, Patricio; Yung-Manriquez, Francisca; Iglesias, Lara Lloret; Vega, José A. (Nature Research, 2023)Correction to: Scientific Reports, published online 10 July 2023 The original version of this Article contained errors in the Affiliations, where Francisco Pérez-Rojas was incorrectly affiliated with ‘Grupo de Investigación ... -
Article
Atypical origin of the left coronary artery originating from the right coronary sinus with interarterial course: A case report (2024)
Pérez-Rojas, Francisco Javier; Lizama, Patricio Maragaño; Maragaño, Maria Ignacia; Opazo, Franco Sepulveda; Osorio, Claudio Tapia; Castro, Trinidad Zerené; Vega, José A. (Elsevier GmbH, 2023)Background: A left coronary artery with atypical origin originating from the right coronary sinus with interarterial course was found in a 68-year-old man, who had been referred to the hemodynamics unit for ventricular ... -
Article
Biometric analysis of healthy coronary arteries in a chilean population: An angiographic study (2020)
Pérez-Rojas, Francisco Javier; Vega, José A.; Gambeta-Tessini, Karla; Puebla-Wuth, Ricardo S.; Olavarría-Solís, Eduardo F.; Maragaño-Lizama, Patricio; Olave, Enrique (Universidad de la Frontera, 2020-12)Thorough knowledge and understanding of coronary arteries and their anatomy is essential when performing cardiac surgery such as a coronary bypass. Coronary angiography is a minimally invasive method used to evaluate the ...