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Predicting Visual Acuity in Patients Treated for AMD

The leading diagnostic tool in modern ophthalmology, Optical Coherence Tomography (OCT), is not yet able to establish the evolution of retinal diseases. Our task is to forecast the progression of retinal diseases by means of machine learning technologies. The aim is to help the ophthalmologist to de...

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Autores principales: Marginean, Beatrice-Andreea, Groza, Adrian, Muntean, George, Nicoara, Simona Delia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221868/
https://www.ncbi.nlm.nih.gov/pubmed/35741314
http://dx.doi.org/10.3390/diagnostics12061504
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author Marginean, Beatrice-Andreea
Groza, Adrian
Muntean, George
Nicoara, Simona Delia
author_facet Marginean, Beatrice-Andreea
Groza, Adrian
Muntean, George
Nicoara, Simona Delia
author_sort Marginean, Beatrice-Andreea
collection PubMed
description The leading diagnostic tool in modern ophthalmology, Optical Coherence Tomography (OCT), is not yet able to establish the evolution of retinal diseases. Our task is to forecast the progression of retinal diseases by means of machine learning technologies. The aim is to help the ophthalmologist to determine when early treatment is needed in order to prevent severe vision impairment or even blindness. The acquired data are made up of sequences of visits from multiple patients with age-related macular degeneration (AMD), which, if not treated at the appropriate time, may result in irreversible blindness. The dataset contains 94 patients with AMD and there are 161 eyes included with more than one medical examination. We used various techniques from machine learning (linear regression, gradient boosting, random forest and extremely randomised trees, bidirectional recurrent neural network, LSTM network, GRU network) to handle technical challenges such as how to learn from small-sized time series, how to handle different time intervals between visits, and how to learn from different numbers of visits for each patient (1–5 visits). For predicting the visual acuity, we performed several experiments with different features. First, by considering only previous measured visual acuity, the best accuracy of 0.96 was obtained based on a linear regression. Second, by considering numerical OCT features such as previous thickness and volume values in all retinal zones, the LSTM network reached the highest score ([Formula: see text]). Third, by considering the fundus scan images represented as embeddings obtained from the convolutional autoencoder, the accuracy was increased for all algorithms. The best forecasting results for visual acuity depend on the number of visits and features used for predictions, i.e., 0.99 for LSTM based on three visits (monthly resampled series) based on numerical OCT values, fundus images, and previous visual acuities.
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spelling pubmed-92218682022-06-24 Predicting Visual Acuity in Patients Treated for AMD Marginean, Beatrice-Andreea Groza, Adrian Muntean, George Nicoara, Simona Delia Diagnostics (Basel) Article The leading diagnostic tool in modern ophthalmology, Optical Coherence Tomography (OCT), is not yet able to establish the evolution of retinal diseases. Our task is to forecast the progression of retinal diseases by means of machine learning technologies. The aim is to help the ophthalmologist to determine when early treatment is needed in order to prevent severe vision impairment or even blindness. The acquired data are made up of sequences of visits from multiple patients with age-related macular degeneration (AMD), which, if not treated at the appropriate time, may result in irreversible blindness. The dataset contains 94 patients with AMD and there are 161 eyes included with more than one medical examination. We used various techniques from machine learning (linear regression, gradient boosting, random forest and extremely randomised trees, bidirectional recurrent neural network, LSTM network, GRU network) to handle technical challenges such as how to learn from small-sized time series, how to handle different time intervals between visits, and how to learn from different numbers of visits for each patient (1–5 visits). For predicting the visual acuity, we performed several experiments with different features. First, by considering only previous measured visual acuity, the best accuracy of 0.96 was obtained based on a linear regression. Second, by considering numerical OCT features such as previous thickness and volume values in all retinal zones, the LSTM network reached the highest score ([Formula: see text]). Third, by considering the fundus scan images represented as embeddings obtained from the convolutional autoencoder, the accuracy was increased for all algorithms. The best forecasting results for visual acuity depend on the number of visits and features used for predictions, i.e., 0.99 for LSTM based on three visits (monthly resampled series) based on numerical OCT values, fundus images, and previous visual acuities. MDPI 2022-06-20 /pmc/articles/PMC9221868/ /pubmed/35741314 http://dx.doi.org/10.3390/diagnostics12061504 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marginean, Beatrice-Andreea
Groza, Adrian
Muntean, George
Nicoara, Simona Delia
Predicting Visual Acuity in Patients Treated for AMD
title Predicting Visual Acuity in Patients Treated for AMD
title_full Predicting Visual Acuity in Patients Treated for AMD
title_fullStr Predicting Visual Acuity in Patients Treated for AMD
title_full_unstemmed Predicting Visual Acuity in Patients Treated for AMD
title_short Predicting Visual Acuity in Patients Treated for AMD
title_sort predicting visual acuity in patients treated for amd
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221868/
https://www.ncbi.nlm.nih.gov/pubmed/35741314
http://dx.doi.org/10.3390/diagnostics12061504
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