Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.

This study aimed to assess the utility of optic nerve head (onh) en-face images, captured with scanning laser ophthalmoscopy (slo) during standard optical coherence tomography (oct) imaging of the posterior segment, and demonstrate the potential of deep learning (dl) ensemble method that operates in...

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Autores principales:	Dominika Sułot, David Alonso-Caneiro, Paweł Ksieniewicz, Patrycja Krzyzanowska-Berkowska, D Robert Iskander
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/77cdf73337d745b49629b1d2f6859210
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spelling	oai:doaj.org-article:77cdf73337d745b49629b1d2f68592102021-11-25T06:23:37ZGlaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.1932-620310.1371/journal.pone.0252339https://doaj.org/article/77cdf73337d745b49629b1d2f68592102021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252339https://doaj.org/toc/1932-6203This study aimed to assess the utility of optic nerve head (onh) en-face images, captured with scanning laser ophthalmoscopy (slo) during standard optical coherence tomography (oct) imaging of the posterior segment, and demonstrate the potential of deep learning (dl) ensemble method that operates in a low data regime to differentiate glaucoma patients from healthy controls. The two groups of subjects were initially categorized based on a range of clinical tests including measurements of intraocular pressure, visual fields, oct derived retinal nerve fiber layer (rnfl) thickness and dilated stereoscopic examination of onh. 227 slo images of 227 subjects (105 glaucoma patients and 122 controls) were used. A new task-specific convolutional neural network architecture was developed for slo image-based classification. To benchmark the results of the proposed method, a range of classifiers were tested including five machine learning methods to classify glaucoma based on rnfl thickness-a well-known biomarker in glaucoma diagnostics, ensemble classifier based on inception v3 architecture, and classifiers based on features extracted from the image. The study shows that cross-validation dl ensemble based on slo images achieved a good discrimination performance with up to 0.962 of balanced accuracy, outperforming all of the other tested classifiers.Dominika SułotDavid Alonso-CaneiroPaweł KsieniewiczPatrycja Krzyzanowska-BerkowskaD Robert IskanderPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252339 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dominika Sułot David Alonso-Caneiro Paweł Ksieniewicz Patrycja Krzyzanowska-Berkowska D Robert Iskander Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.
description	This study aimed to assess the utility of optic nerve head (onh) en-face images, captured with scanning laser ophthalmoscopy (slo) during standard optical coherence tomography (oct) imaging of the posterior segment, and demonstrate the potential of deep learning (dl) ensemble method that operates in a low data regime to differentiate glaucoma patients from healthy controls. The two groups of subjects were initially categorized based on a range of clinical tests including measurements of intraocular pressure, visual fields, oct derived retinal nerve fiber layer (rnfl) thickness and dilated stereoscopic examination of onh. 227 slo images of 227 subjects (105 glaucoma patients and 122 controls) were used. A new task-specific convolutional neural network architecture was developed for slo image-based classification. To benchmark the results of the proposed method, a range of classifiers were tested including five machine learning methods to classify glaucoma based on rnfl thickness-a well-known biomarker in glaucoma diagnostics, ensemble classifier based on inception v3 architecture, and classifiers based on features extracted from the image. The study shows that cross-validation dl ensemble based on slo images achieved a good discrimination performance with up to 0.962 of balanced accuracy, outperforming all of the other tested classifiers.
format	article
author	Dominika Sułot David Alonso-Caneiro Paweł Ksieniewicz Patrycja Krzyzanowska-Berkowska D Robert Iskander
author_facet	Dominika Sułot David Alonso-Caneiro Paweł Ksieniewicz Patrycja Krzyzanowska-Berkowska D Robert Iskander
author_sort	Dominika Sułot
title	Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.
title_short	Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.
title_full	Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.
title_fullStr	Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.
title_full_unstemmed	Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.
title_sort	glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/77cdf73337d745b49629b1d2f6859210
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Glaucoma classification based on scanning laser ophthalmoscopic images using a deep learning ensemble method.

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