Trans-orbital sonography versus visual evoked potentials in acute demyelinating optic neuritis

Elkholy, Saly H; El-Jaafary, Shaimaa I; Kotb, Mohamed; Gohary, Amira M. El; Elbhy, Bodour A.

doi:10.1016/j.msard.2020.101934

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…The objective assessment of VA, therefore, is a classic common challenge for medical ophthalmologists. Pattern-reversal visual-evoked potentials (PRVEPs) have been widely used clinically as an objective electrophysiological examination in the assessment of optic neuropathy [2][3][4] and functional visual loss [5], in the identi cation of malingering [6], and in the early diagnosis and prognostication of multiple sclerosis [7][8][9]. The P100 wave is the rst positive wave to appear in PRVEPs and includes both peak time and amplitude components, with low variability across subjects and a strong relationship with VA. As a sub eld of arti cial intelligence, machine learning has been increasingly applied to medical practice since it can extract deeper features from raw data and combine several predictors in a highly interactive manner [10].…”

Section: Introductionmentioning

confidence: 99%

Machine learning models for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials

Chen

et al. 2022

Preprint

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BackgroundTo explore the development of machine learning models for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials (PRVEPs) as a reference for improving medical selection and identi cation methods for aircrew visual function. MethodsTwenty-four volunteers were recruited and forty-eight eyes were divided into four groups of 1.0, 0.8, 0.6, and 0.4 (decimal vision) according to visual acuity. The relationship between visual acuity, peak time, or amplitude of P100 that was recorded at 5.7°, 2.6°, 1°, 34′, 15′, and 7′ visual angles was analyzed using repeated-measures analysis of variance. Receiver operating characteristic (ROC) curves were used to compare the effectiveness of the P100 peak time and amplitude as objective indicators of visual acuity at different viewing angles. Based on meaningful P100 peak time and amplitude, four machine learning algorithms were used to construct objective assessment models for visual acuity, and the e cacy of different models was compared by repeated sampling comparisons and ten-fold cross-validation. ResultsThe main effects of P100 peak time and amplitude between different visual acuity and various viewing angles were statistically signi cant (P<0.05). Visual acuity in general was negatively correlated with peak time and positively correlated with amplitude. The peak time initially shortened with increasing examination angle and gradually lengthened after the minimum value was reached at 1°. At the 1° visual angle, there were statistically signi cant differences when comparing the peak times between the vision groups with each other (P<0.05), and the amplitudes of the vision reduction groups were signi cantly lower than that of the 1.0 vision group (P<0.05). The correlations between peak time, amplitude, and visual acuity were all highest at 1° (r = 0.740, 0.438). For different binarization classi cations of visual acuity, the maximum area under the ROC curves (AUC) of the peak time or amplitude at all visual angles was higher than 0.8 (P < 0.001). Among the objective assessment models of visual acuity constructed by different machine learning algorithms, multilayer perceptron (MLP) had the best performance in distinguishing 1.0 from 0.8, 0.6, and 0.4 visual acuities, with an accuracy of 93.75% for ten-fold crossvalidation. ConclusionsThe P100 peak time and amplitude obtained at multiple views of PRVEPs are correlated with visual acuity; machine learning models based on this can be used to assist in the objective evaluation of visual

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Section: Introductionmentioning

confidence: 99%

Machine learning models for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials

Chen

et al. 2022

Preprint

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Transorbital Sonography for Monitoring Neuromyelitis Optica: a Case Report

Montabone¹,

Nardone

Siniscalchi

et al. 2022

SN Compr. Clin. Med.

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A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm

Chen

et al. 2023

BMC Ophthalmol

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Background To develop machine learning models for objectively evaluating visual acuity (VA) based on pattern-reversal visual evoked potentials (PRVEPs) and other related visual parameters. Methods Twenty-four volunteers were recruited and forty-eight eyes were divided into four groups of 1.0, 0.8, 0.6, and 0.4 (decimal vision). The relationship between VA, peak time, or amplitude of P100 recorded at 5.7°, 2.6°, 1°, 34′, 15′, and 7′ check sizes were analyzed using repeated-measures analysis of variance. Correlations between VA and P100, contrast sensitivity (CS), refractive error, wavefront aberrations, and visual field were analyzed by rank correlation. Based on meaningful P100 peak time, P100 amplitude, and other related visual parameters, four machine learning algorithms and an ensemble classification algorithm were used to construct objective assessment models for VA. Receiver operating characteristic (ROC) curves were used to compare the efficacy of different models by repeated sampling comparisons and ten-fold cross-validation. Results The main effects of P100 peak time and amplitude between different VA and check sizes were statistically significant (all P < 0.05). Except amplitude at 2.6° and 5.7°, VA was negatively correlated with peak time and positively correlated with amplitude. The peak time initially shortened with increasing check size and gradually lengthened after the minimum value was reached at 1°. At the 1° check size, there were statistically significant differences when comparing the peak times between the vision groups with each other (all P < 0.05), and the amplitudes of the vision reduction groups were significantly lower than that of the 1.0 vision group (all P < 0.01). The correlations between peak time, amplitude, and visual acuity were all highest at 1° (rs = − 0.740, 0.438). VA positively correlated with CS and spherical equivalent (all P < 0.001). There was a negative correlation between VA and coma aberrations (P < 0.05). For different binarization classifications of VA, the classifier models with the best assessment efficacy all had the mean area under the ROC curves (AUC) above 0.95 for 500 replicate samples and above 0.84 for ten-fold cross-validation. Conclusions Machine learning models established by meaning visual parameters related to visual acuity can assist in the objective evaluation of VA.

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Trans-orbital sonography versus visual evoked potentials in acute demyelinating optic neuritis

Cited by 4 publications

References 21 publications

Machine learning models for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials

Machine learning models for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials

Transorbital Sonography for Monitoring Neuromyelitis Optica: a Case Report

A feasibility study for objective evaluation of visual acuity based on pattern-reversal visual evoked potentials and other related visual parameters with machine learning algorithm

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