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    Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma by Zapata MA, Royo-Fibla D, Font O, Vela JI, Marcantonio I, Moya-Sánchez EU, Sánchez-Pérez A, Garcia-Gasulla D, Cortés U, Ayguadé E, Labarta J

    Published 2020-02-01
    “…Miguel Angel Zapata,1 Dídac Royo-Fibla,1 Octavi Font,1 José Ignacio Vela,2,3 Ivanna Marcantonio,2,3 Eduardo Ulises Moya-Sánchez,4,5 Abraham Sánchez-Pérez,5 Darío Garcia-Gasulla,4 Ulises Cortés,4,6 Eduard Ayguadé,4,6 Jesus Labarta4,6 1Optretina, Barcelona, Spain; 2Ophthalmology Department, Hospital de la Santa Creu I de Sant Pau, Barcelona 08041, Spain; 3Universitat Autònoma de Barcelona (UAB), Campus de la UAB, Barcelona, Spain; 4Barcelona Supercomputing Center (BSC), Barcelona, Spain; 5Universidad Autónoma de Guadalajara - Postgrado en Ciencias Computacionales, Guadalajara, Mexico; 6Universitat Politècnica de Catalunya - BarcelonaTECH, Campus Nord, Barcelona, SpainCorrespondence: Miguel Angel ZapataOptretina, C/ Las Palmas 11, 08195 Sant Cugat del Vallès, Barcelona, SpainTel +34 655809682Email mazapata@optretina.comPurpose: To assess the performance of deep learning algorithms for different tasks in retinal fundus images: (1) detection of retinal fundus images versus optical coherence tomography (OCT) or other images, (2) evaluation of good quality retinal fundus images, (3) distinction between right eye (OD) and left eye (OS) retinal fundus images,(4) detection of age-related macular degeneration (AMD) and (5) detection of referable glaucomatous optic neuropathy (GON).Patients and Methods: Five algorithms were designed. …”
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