Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, w...

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Autores principales:	Arun Govindaiah, Abdul Baten, R. Theodore Smith, Siva Balasubramanian, Alauddin Bhuiyan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	macular degeneration genetics fundus imaging deep learning Medicine R
Acceso en línea:	https://doaj.org/article/34f34a7b62b04458a3e3a4a4c83ad2a4
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spelling	oai:doaj.org-article:34f34a7b62b04458a3e3a4a4c83ad2a42021-11-25T18:07:24ZOptimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration10.3390/jpm111111272075-4426https://doaj.org/article/34f34a7b62b04458a3e3a4a4c83ad2a42021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1127https://doaj.org/toc/2075-4426Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2–5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.Arun GovindaiahAbdul BatenR. Theodore SmithSiva BalasubramanianAlauddin BhuiyanMDPI AGarticlemacular degenerationgeneticsfundus imagingdeep learningMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1127, p 1127 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	macular degeneration genetics fundus imaging deep learning Medicine R
spellingShingle	macular degeneration genetics fundus imaging deep learning Medicine R Arun Govindaiah Abdul Baten R. Theodore Smith Siva Balasubramanian Alauddin Bhuiyan Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration
description	Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. In this study, we compare the performance of retinal fundus images and genetic-information-based machine learning models for the prediction of late AMD. Using data from the Age-related Eye Disease Study, we built machine learning models with various combinations of genetic, socio-demographic/clinical, and retinal image data to predict late AMD using its severity and category in a single visit, in 2, 5, and 10 years. We compared their performance in sensitivity, specificity, accuracy, and unweighted kappa. The 2-year model based on retinal image and socio-demographic (S-D) parameters achieved a sensitivity of 91.34%, specificity of 84.49% while the same for genetic and S-D-parameters-based model was 79.79% and 66.84%. For the 5-year model, the retinal image and S-D-parameters-based model also outperformed the genetic and S-D parameters-based model. The two 10-year models achieved similar sensitivities of 74.24% and 75.79%, respectively, but the retinal image and S-D-parameters-based model was otherwise superior. The retinal-image-based models were not further improved by adding genetic data. Retinal imaging and S-D data can build an excellent machine learning predictor of developing late AMD over 2–5 years; the retinal imaging model appears to be the preferred prognostic tool for efficient patient management.
format	article
author	Arun Govindaiah Abdul Baten R. Theodore Smith Siva Balasubramanian Alauddin Bhuiyan
author_facet	Arun Govindaiah Abdul Baten R. Theodore Smith Siva Balasubramanian Alauddin Bhuiyan
author_sort	Arun Govindaiah
title	Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration
title_short	Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration
title_full	Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration
title_fullStr	Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration
title_full_unstemmed	Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration
title_sort	optimized prediction models from fundus imaging and genetics for late age-related macular degeneration
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/34f34a7b62b04458a3e3a4a4c83ad2a4
work_keys_str_mv	AT arungovindaiah optimizedpredictionmodelsfromfundusimagingandgeneticsforlateagerelatedmaculardegeneration AT abdulbaten optimizedpredictionmodelsfromfundusimagingandgeneticsforlateagerelatedmaculardegeneration AT rtheodoresmith optimizedpredictionmodelsfromfundusimagingandgeneticsforlateagerelatedmaculardegeneration AT sivabalasubramanian optimizedpredictionmodelsfromfundusimagingandgeneticsforlateagerelatedmaculardegeneration AT alauddinbhuiyan optimizedpredictionmodelsfromfundusimagingandgeneticsforlateagerelatedmaculardegeneration
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Optimized Prediction Models from Fundus Imaging and Genetics for Late Age-Related Macular Degeneration

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