Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma

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,...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2020
Materias:	artificial intelligence retinal diseases screening retinal fundus image Ophthalmology RE1-994
Acceso en línea:	https://doaj.org/article/6a95ffbc13704f83b8965f1e0ae07db4
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spelling	oai:doaj.org-article:6a95ffbc13704f83b8965f1e0ae07db42021-12-02T03:32:52ZArtificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma1177-5483https://doaj.org/article/6a95ffbc13704f83b8965f1e0ae07db42020-02-01T00:00:00Zhttps://www.dovepress.com/artificial-intelligence-to-identify-retinal-fundus-images-quality-vali-peer-reviewed-article-OPTHhttps://doaj.org/toc/1177-5483Miguel 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. 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.Keywords: artificial intelligence, retinal diseases, screening, retinal fundus imageZapata MARoyo-Fibla DFont OVela JIMarcantonio IMoya-Sánchez EUSánchez-Pérez AGarcia-Gasulla DCortés UAyguadé ELabarta JDove Medical Pressarticleartificial intelligence retinal diseases screening retinal fundus imageOphthalmologyRE1-994ENClinical Ophthalmology, Vol Volume 14, Pp 419-429 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence retinal diseases screening retinal fundus image Ophthalmology RE1-994
spellingShingle	artificial intelligence retinal diseases screening retinal fundus image Ophthalmology RE1-994 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 Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
description	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. 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.Keywords: artificial intelligence, retinal diseases, screening, retinal fundus image
format	article
author	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
author_facet	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
author_sort	Zapata MA
title	Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
title_short	Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
title_full	Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
title_fullStr	Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
title_full_unstemmed	Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
title_sort	artificial intelligence to identify retinal fundus images, quality validation, laterality evaluation, macular degeneration, and suspected glaucoma
publisher	Dove Medical Press
publishDate	2020
url	https://doaj.org/article/6a95ffbc13704f83b8965f1e0ae07db4
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Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma

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