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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9221868 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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