Autor según el artículo: Romero-Aroca, Pedro; Verges-Puig, Raquel; de la Torre, Jordi; Valls, Aida; Relano-Barambio, Naiara; Puig, Domenec; Baget-Bernaldiz, Marc;

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: Puig Valls, Domènec Savi / Romero Aroca, Pedro / Valls Mateu, Aïda

Palabras clave: System Screening of diabetic retinopathy Prevalence Guidelines Family physicians Diabetic retinopathy Deep learning Convolutional neural network Agreement diabetic retinopathy deep learning convolutional neural network

Resumen: Background: To validate our deep learning algorithm (DLA) to read diabetic retinopathy (DR) retinographies. Introduction: Currently DR detection is made by retinography; due to its increasing diabetes mellitus incidence we need to find systems that help us to screen DR. Materials and Methods: The DLA was built and trained using 88,702 images from EyePACS, 1,748 from Messidor-2, and 19,230 from our own population. For validation a total of 38,339 retinographies from 17,669 patients (obtained from our DR screening databases) were read by a DLA and compared by four senior retina ophthalmologists for detecting any-DR and referable-DR. We determined the values of Cohen's weighted Kappa (CWK) index, sensitivity (S), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV), and errors type I and II. Results: The results of the DLA to detect any-DR were: CWK = 0.886 +/- 0.004 (95% confidence interval [CI] 0.879-0.894), S = 0.967%, SP = 0.976%, PPV = 0.836%, and NPV = 0.996%. The error type I = 0.024, and the error type II = 0.004. Likewise, the referable-DR results were: CWK = 0.809 (95% CI 0.798-0.819), S = 0.998, SP = 0.968, PPV = 0.701, NPV = 0.928, error type I = 0.032, and error type II = 0.001. Discussion: Our DLA can be used as a high confidence diagnostic tool to help in DR screening, especially when it might be difficult for ophthalmologists or other professionals to identify. It can identify patients with any-DR and those that should be referred. Conclusions: The DLA can be valid to aid in screening of DR.

Áreas temáticas: Saúde coletiva Odontología Medicine (miscellaneous) Medicina iii Medicina ii Medicina i Interdisciplinar Health information management Health informatics Health care sciences & services General medicine Engenharias iv Engenharias ii Enfermagem Educação física Ciência da computação Biotecnología Astronomia / física Arquitetura, urbanismo e design

ISSN: 15305627

Direcció de correo del autor: aida.valls@urv.cat pedro.romero@urv.cat domenec.puig@urv.cat

Identificador del autor: 0000-0003-3616-7809 0000-0002-7061-8987 0000-0002-0562-4205

Fecha de alta del registro: 2023-02-18

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Telemedicine And E-Health. 26 (8): 1001-1009

Referencia de l'ítem segons les normes APA: Romero-Aroca, Pedro; Verges-Puig, Raquel; de la Torre, Jordi; Valls, Aida; Relano-Barambio, Naiara; Puig, Domenec; Baget-Bernaldiz, Marc; (2020). Validation of a Deep Learning Algorithm for Diabetic Retinopathy. Telemedicine And E-Health, 26(8), 1001-1009. DOI: 10.1089/tmj.2019.0137

DOI del artículo: 10.1089/tmj.2019.0137

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications