Autor según el artículo: Baget-Bernaldiz, Marc; Pedro, Romero-Aroca; Santos-Blanco, Esther; Navarro-Gil, Raul; Valls, Aida; Moreno, Antonio; Rashwan, Hatem A; Puig, Domenec
Departamento: Medicina i Cirurgia
Autor/es de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Baget Bernaldiz, Marc / Moreno Ribas, Antonio / Navarro Gil, Raúl / Puig Valls, Domènec Savi / Romero Aroca, Pedro / Valls Mateu, Aïda
Palabras clave: Validation Sensitivity and specificity Retinography Retina macula lutea Retina image Receiver operating characteristic Prevalence Predictive value Ophthalmologist Non insulin dependent diabetes mellitus Middle aged Male Major clinical study Learning algorithm Image quality Image assessment software Human Guidelines Female False positive result False negative result Eye photography Disease severity Diagnostic test accuracy study Diagnostic accuracy Diabetic retinopathy screening Diabetic retinopathy Diabetic patient Deep learning algorithm Deep learning Data base Convolutional neural networks Convolutional neural network Computer assisted diagnosis Cohort analysis Classifier Classification algorithm Article Aged Adult
Resumen: Background: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). Results: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. Conclusions: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.
Áreas temáticas: Medicine, general & internal Internal medicine Clinical biochemistry
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: marc.baget@urv.cat hatem.abdellatif@urv.cat raul.navarrog@estudiants.urv.cat antonio.moreno@urv.cat domenec.puig@urv.cat pedro.romero@urv.cat aida.valls@urv.cat
Identificador del autor: 0000-0001-5421-1637 0000-0003-2970-205X 0000-0003-3945-2314 0000-0002-0562-4205 0000-0002-7061-8987 0000-0003-3616-7809
Fecha de alta del registro: 2024-09-21
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Diagnostics. 11 (8): 1385-
Referencia de l'ítem segons les normes APA: Baget-Bernaldiz, Marc; Pedro, Romero-Aroca; Santos-Blanco, Esther; Navarro-Gil, Raul; Valls, Aida; Moreno, Antonio; Rashwan, Hatem A; Puig, Domenec (2021). Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database. Diagnostics, 11(8), 1385-. DOI: 10.3390/diagnostics11081385
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2021
Tipo de publicación: Journal Publications
Repositori URV