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Artificial intelligence to identify retinal fundus images, quality validation, laterality evaluation, macular degeneration, and suspected glaucoma

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Zapata Victori, Miguel Ángel
Royo Fibla, Dídac
Font, Octavi
Vela Segarra, José Ignacio
Marcantonio Santa Cruz, Ivanna Andrea
Moya Sánchez, Eduardo Ulises
Sánchez Pérez, Abraham
Garcia Gasulla, DarioMés informacióMés informacióMés informació
Cortés García, Claudio UlisesMés informacióMés informacióMés informació
Ayguadé Parra, EduardMés informacióMés informacióMés informació
Labarta Mancho, Jesús JoséMés informacióMés informacióMés informació
Document typeArticle
Defense date2020-02-13
Rights accessOpen Access
Attribution-NonCommercial 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial 3.0 Spain
Abstract
Purpose: 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. Retrospective study from a database of 306,302 images, Optretina’s tagged dataset. Three different ophthalmologists, all retinal specialists, classified all images. The dataset was split per patient in a training (80%) and testing (20%) splits. Three different CNN architectures were employed, two of which were custom designed to minimize the number of parameters with minimal impact on its accuracy. Main outcome measure was area under the curve (AUC) with accuracy, sensitivity and specificity. Results: Determination of retinal fundus image had AUC of 0.979 with an accuracy of 96% (sensitivity 97.7%, specificity 92.4%). Determination of good quality retinal fundus image had AUC of 0.947, accuracy 91.8% (sensitivity 96.9%, specificity 81.8%). Algorithm for OD/OS had AUC 0.989, accuracy 97.4%. AMD had AUC of 0.936, accuracy 86.3% (sensitivity 90.2% specificity 82.5%), GON had AUC of 0.863, accuracy 80.2% (sensitivity 76.8%, specificity 83.8%). Conclusion: Deep learning algorithms can differentiate a retinal fundus image from other images. Algorithms can evaluate the quality of an image, discriminate between right or left eye and detect the presence of AMD and GON with a high level of accuracy, sensitivity and specificity.
CitationZapata, M. [et al.]. Artificial intelligence to identify retinal fundus images, quality validation, laterality evaluation, macular degeneration, and suspected glaucoma. "Clinical ophthalmology", 13 Febrer 2020, vol. 14, p. 419-428. 
URIhttp://hdl.handle.net/2117/181680
DOI10.2147/OPTH.S235751
ISSN1177-5483
Publisher versionhttps://www.dovepress.com/artificial-intelligence-to-identify-retinal-fundus-images-quality-vali-peer-reviewed-article-OPTH
Collections
  • Computer Sciences - Articles de revista [272]
  • Departament d'Arquitectura de Computadors - Articles de revista [957]
  • Departament de Ciències de la Computació - Articles de revista [941]
  • KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic - Articles de revista [112]
  • CAP - Grup de Computació d'Altes Prestacions - Articles de revista [380]
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