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Estimating visual field loss from monoscopic optic disc photography using deep learning model

Visual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test–retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of standard automated perimetry (SAP) from monoscopic op...

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Autores principales: Lee, Jinho, Kim, Yong Woo, Ha, Ahnul, Kim, Young Kook, Park, Ki Ho, Choi, Hyuk Jin, Jeoung, Jin Wook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712913/
https://www.ncbi.nlm.nih.gov/pubmed/33273643
http://dx.doi.org/10.1038/s41598-020-78144-1
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author Lee, Jinho
Kim, Yong Woo
Ha, Ahnul
Kim, Young Kook
Park, Ki Ho
Choi, Hyuk Jin
Jeoung, Jin Wook
author_facet Lee, Jinho
Kim, Yong Woo
Ha, Ahnul
Kim, Young Kook
Park, Ki Ho
Choi, Hyuk Jin
Jeoung, Jin Wook
author_sort Lee, Jinho
collection PubMed
description Visual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test–retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of standard automated perimetry (SAP) from monoscopic optic disc photographs (ODPs). A total of 1200 image pairs (ODPs and SAP results) for 563 eyes of 327 participants were enrolled. A DL model was built by combining a pre-trained DL network and subsequently trained fully connected layers. The correlation coefficient and mean absolute error (MAE) between the predicted and measured MDs were calculated. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the detection ability for glaucomatous visual field (VF) loss. The data were split into training/validation (1000 images) and testing (200 images) sets to evaluate the performance of the algorithm. The predicted MD showed a strong correlation and good agreement with the actual MD (correlation coefficient = 0.755; R(2) = 57.0%; MAE = 1.94 dB). The model also accurately predicted the presence of glaucomatous VF loss (AUC 0.953). The DL algorithm showed great feasibility for prediction of MD and detection of glaucomatous functional loss from ODPs.
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spelling pubmed-77129132020-12-03 Estimating visual field loss from monoscopic optic disc photography using deep learning model Lee, Jinho Kim, Yong Woo Ha, Ahnul Kim, Young Kook Park, Ki Ho Choi, Hyuk Jin Jeoung, Jin Wook Sci Rep Article Visual field assessment is recognized as the important criterion of glaucomatous damage judgement; however, it can show large test–retest variability. We developed a deep learning (DL) algorithm that quantitatively predicts mean deviation (MD) of standard automated perimetry (SAP) from monoscopic optic disc photographs (ODPs). A total of 1200 image pairs (ODPs and SAP results) for 563 eyes of 327 participants were enrolled. A DL model was built by combining a pre-trained DL network and subsequently trained fully connected layers. The correlation coefficient and mean absolute error (MAE) between the predicted and measured MDs were calculated. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the detection ability for glaucomatous visual field (VF) loss. The data were split into training/validation (1000 images) and testing (200 images) sets to evaluate the performance of the algorithm. The predicted MD showed a strong correlation and good agreement with the actual MD (correlation coefficient = 0.755; R(2) = 57.0%; MAE = 1.94 dB). The model also accurately predicted the presence of glaucomatous VF loss (AUC 0.953). The DL algorithm showed great feasibility for prediction of MD and detection of glaucomatous functional loss from ODPs. Nature Publishing Group UK 2020-12-03 /pmc/articles/PMC7712913/ /pubmed/33273643 http://dx.doi.org/10.1038/s41598-020-78144-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lee, Jinho
Kim, Yong Woo
Ha, Ahnul
Kim, Young Kook
Park, Ki Ho
Choi, Hyuk Jin
Jeoung, Jin Wook
Estimating visual field loss from monoscopic optic disc photography using deep learning model
title Estimating visual field loss from monoscopic optic disc photography using deep learning model
title_full Estimating visual field loss from monoscopic optic disc photography using deep learning model
title_fullStr Estimating visual field loss from monoscopic optic disc photography using deep learning model
title_full_unstemmed Estimating visual field loss from monoscopic optic disc photography using deep learning model
title_short Estimating visual field loss from monoscopic optic disc photography using deep learning model
title_sort estimating visual field loss from monoscopic optic disc photography using deep learning model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712913/
https://www.ncbi.nlm.nih.gov/pubmed/33273643
http://dx.doi.org/10.1038/s41598-020-78144-1
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