Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis

PURPOSE: To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis. DESIGN: Cross-sectional analysis of a retinal i...

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Detalles Bibliográficos
Autores principales: Carrera-Escalé, Laura, Benali, Anass, Rathert, Ann-Christin, Martín-Pinardel, Ruben, Bernal-Morales, Carolina, Alé-Chilet, Anibal, Barraso, Marina, Marín-Martinez, Sara, Feu-Basilio, Silvia, Rosinés-Fonoll, Josep, Hernandez, Teresa, Vilá, Irene, Castro-Dominguez, Rafael, Oliva, Cristian, Vinagre, Irene, Ortega, Emilio, Gimenez, Marga, Vellido, Alfredo, Romero, Enrique, Zarranz-Ventura, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Artificial Intelligence and Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791596/
https://www.ncbi.nlm.nih.gov/pubmed/36578904
http://dx.doi.org/10.1016/j.xops.2022.100259

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791596/
https://www.ncbi.nlm.nih.gov/pubmed/36578904
http://dx.doi.org/10.1016/j.xops.2022.100259

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