Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device

The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based r...

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Autores principales:	Tina Diao, Fareshta Kushzad, Megh D. Patel, Megha P. Bindiganavale, Munam Wasi, Mykel J. Kochenderfer, Heather E. Moss
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	photopic negative response (PhNR) electroretinogram (ERG) optic neuropathy classification machine learning time series analysis Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/9b6f57861e6740ffb68fc7c9b5595ad8
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spelling	oai:doaj.org-article:9b6f57861e6740ffb68fc7c9b5595ad82021-12-03T05:12:14ZComparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device2296-858X10.3389/fmed.2021.771713https://doaj.org/article/9b6f57861e6740ffb68fc7c9b5595ad82021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmed.2021.771713/fullhttps://doaj.org/toc/2296-858XThe photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.Tina DiaoFareshta KushzadMegh D. PatelMegha P. BindiganavaleMunam WasiMykel J. KochenderferMykel J. KochenderferHeather E. MossHeather E. MossFrontiers Media S.A.articlephotopic negative response (PhNR)electroretinogram (ERG)optic neuropathyclassificationmachine learningtime series analysisMedicine (General)R5-920ENFrontiers in Medicine, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	photopic negative response (PhNR) electroretinogram (ERG) optic neuropathy classification machine learning time series analysis Medicine (General) R5-920
spellingShingle	photopic negative response (PhNR) electroretinogram (ERG) optic neuropathy classification machine learning time series analysis Medicine (General) R5-920 Tina Diao Fareshta Kushzad Megh D. Patel Megha P. Bindiganavale Munam Wasi Mykel J. Kochenderfer Mykel J. Kochenderfer Heather E. Moss Heather E. Moss Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device
description	The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.
format	article
author	Tina Diao Fareshta Kushzad Megh D. Patel Megha P. Bindiganavale Munam Wasi Mykel J. Kochenderfer Mykel J. Kochenderfer Heather E. Moss Heather E. Moss
author_facet	Tina Diao Fareshta Kushzad Megh D. Patel Megha P. Bindiganavale Munam Wasi Mykel J. Kochenderfer Mykel J. Kochenderfer Heather E. Moss Heather E. Moss
author_sort	Tina Diao
title	Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device
title_short	Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device
title_full	Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device
title_fullStr	Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device
title_full_unstemmed	Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device
title_sort	comparison of machine learning approaches to improve diagnosis of optic neuropathy using photopic negative response measured using a handheld device
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/9b6f57861e6740ffb68fc7c9b5595ad8
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Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device

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