Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model

Abstract We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and spectral domain-optical coherence tomograph...

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Autores principales:	Jinho Lee, Young Kook Kim, Ahnul Ha, Sukkyu Sun, Yong Woo Kim, Jin-Soo Kim, Jin Wook Jeoung, Ki Ho Park
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/13bcdaeaa2c84e4db26f61e511de5669
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spelling	oai:doaj.org-article:13bcdaeaa2c84e4db26f61e511de56692021-12-02T10:59:52ZMacular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model10.1038/s41598-020-60277-y2045-2322https://doaj.org/article/13bcdaeaa2c84e4db26f61e511de56692020-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-60277-yhttps://doaj.org/toc/2045-2322Abstract We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and spectral domain-optical coherence tomography (SD-OCT) scans for 431 eyes of 259 participants (183 eyes of 114 healthy controls, 68 eyes of 46 glaucoma suspects, and 180 eyes of 99 glaucoma patients) were enrolled. An HDLM was built by combining a pre-trained deep learning network and support vector machine. The correlation coefficient and mean absolute error (MAE) between the predicted and measured mGCIPL thicknesses were calculated. The measured (OCT-based) and predicted (HDLM-based) average mGCIPL thicknesses were 73.96 ± 8.81 µm and 73.92 ± 7.36 µm, respectively (P = 0.844). The predicted mGCIPL thickness showed a strong correlation and good agreement with the measured mGCIPL thickness (Correlation coefficient r = 0.739; P < 0.001; MAE = 4.76 µm). Even when the peripapillary area (diameter: 1.5 disc diameters) was masked, the correlation (r = 0.713; P < 0.001) and agreement (MAE = 4.87 µm) were not changed significantly (P = 0.378 and 0.724, respectively). The trained HDLM algorithm showed a great capability for mGCIPL thickness prediction from RNFLPs.Jinho LeeYoung Kook KimAhnul HaSukkyu SunYong Woo KimJin-Soo KimJin Wook JeoungKi Ho ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jinho Lee Young Kook Kim Ahnul Ha Sukkyu Sun Yong Woo Kim Jin-Soo Kim Jin Wook Jeoung Ki Ho Park Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
description	Abstract We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and spectral domain-optical coherence tomography (SD-OCT) scans for 431 eyes of 259 participants (183 eyes of 114 healthy controls, 68 eyes of 46 glaucoma suspects, and 180 eyes of 99 glaucoma patients) were enrolled. An HDLM was built by combining a pre-trained deep learning network and support vector machine. The correlation coefficient and mean absolute error (MAE) between the predicted and measured mGCIPL thicknesses were calculated. The measured (OCT-based) and predicted (HDLM-based) average mGCIPL thicknesses were 73.96 ± 8.81 µm and 73.92 ± 7.36 µm, respectively (P = 0.844). The predicted mGCIPL thickness showed a strong correlation and good agreement with the measured mGCIPL thickness (Correlation coefficient r = 0.739; P < 0.001; MAE = 4.76 µm). Even when the peripapillary area (diameter: 1.5 disc diameters) was masked, the correlation (r = 0.713; P < 0.001) and agreement (MAE = 4.87 µm) were not changed significantly (P = 0.378 and 0.724, respectively). The trained HDLM algorithm showed a great capability for mGCIPL thickness prediction from RNFLPs.
format	article
author	Jinho Lee Young Kook Kim Ahnul Ha Sukkyu Sun Yong Woo Kim Jin-Soo Kim Jin Wook Jeoung Ki Ho Park
author_facet	Jinho Lee Young Kook Kim Ahnul Ha Sukkyu Sun Yong Woo Kim Jin-Soo Kim Jin Wook Jeoung Ki Ho Park
author_sort	Jinho Lee
title	Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
title_short	Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
title_full	Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
title_fullStr	Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
title_full_unstemmed	Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model
title_sort	macular ganglion cell-inner plexiform layer thickness prediction from red-free fundus photography using hybrid deep learning model
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/13bcdaeaa2c84e4db26f61e511de5669
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Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model

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