Novel Machine-Learning Based Framework Using Electroretinography Data for the Detection of Early-Stage Glaucoma

Abstract

PurposeEarly-stage glaucoma diagnosis has been a challenging problem in ophthalmology. The current state-of-the-art glaucoma diagnosis techniques do not completely leverage the functional measures' such as electroretinogram's immense potential; instead, focus is on structural measures like optical coherence tomography. The current study aims to take a foundational step toward the development of a novel and reliable predictive framework for early detection of glaucoma using machine-learning-based algorithm capable of leveraging medically relevant information that ERG signals contain.MethodsERG signals from 60 eyes of DBA/2 mice were grouped for binary classification based on age. The signals were also grouped based on intraocular pressure (IOP) for multiclass classification. Statistical and wavelet-based features were engineered and extracted. Important predictors (ERG tests and features) were determined, and the performance of five machine learning-based methods were evaluated.ResultsRandom forest (bagged trees) ensemble classifier provided the best performance in both binary and multiclass classification of ERG signals. An accuracy of 91.7 and 80% was achieved for binary and multiclass classification, respectively, suggesting that machine-learning-based models can detect subtle changes in ERG signals if trained using advanced features such as those based on wavelet analyses.ConclusionsThe present study describes a novel, machine-learning-based method to analyze ERG signals providing additional information that may be used to detect early-stage glaucoma. Based on promising performance metrics obtained using the proposed machine-learning-based framework leveraging an established ERG data set, we conclude that the novel framework allows for detection of functional deficits of early/various stages of glaucoma in mice.

Discrete Wavelet Transform Analysis of the Electroretinogram in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

“…Signal analysis of the ERG waveform may also provide more subtle functional insights into the structural changes within the retina which have been documented in longitudinal studies of Alzheimer’s or Parkinson’s disease using retinal imaging techniques ( Kashani et al, 2021 ). It may also provide a sensitive method to monitor the effects of clinical trials of pharmacological and gene therapy to manage retinal and ophthalmic diseases ( Yu-Wai-Man et al, 2020 ; Maguire et al, 2021 ; Gajendran et al, 2022 ).…”

Visual electrophysiology and “the potential of the potentials”

“…With development of newer devices (including portable and multimodal technology), and more refined, including more rapid, testing protocols, combined with novel, AI-assisted analyses, it is likely that tests will become more accessible and continue to yield valuable clinical and scientific information. This will have relevance to common and rare diseases of the eye and visual pathway, and also, given similarities between retinal and brain circuitry, potentially to wider neurological and neuropsychiatric disease [71,85,88,[92][93][94].…”

“…Artificial intelligence (AI) is being applied to many areas of healthcare showing high levels of accuracy, equivalent to experts. There have been investigations applying AI or machine-learning techniques to electrophysiology data [34,[85][86][87][88][89][90]. These include studies of ERG data that may have applicability in conditions including glaucoma [88], hydroxychloroquine retinopathy [87], and even autism spectrum disorder [86] and depression [89], well as studies of VEP data to improve estimation of acuity [90].…”

“…There have been investigations applying AI or machine-learning techniques to electrophysiology data [34,[85][86][87][88][89][90]. These include studies of ERG data that may have applicability in conditions including glaucoma [88], hydroxychloroquine retinopathy [87], and even autism spectrum disorder [86] and depression [89], well as studies of VEP data to improve estimation of acuity [90]. A recent study demonstrated machine learning techniques could be successfully applied to develop automated ERG phenotypic classification of patients with Stargardt disease (arising from variants in the ABCA4 gene) [85].…”

Retinal electrophysiology in central nervous system disorders. A review of human and mouse studies

“…The PERG has two components: the N95 is defined by retinal ganglion cells (RGCs Extracting features from signal analysis in combination with machine learning algorithms can identify neurological conditions such as ASD (Mohammad-Manjur et al, 2022) and depression (Schwitzer et al, 2022a). Machine learning has been used successfully to classify glaucoma in mouse models and in the future may provide a powerful tool to help in classification of psychiatric conditions based on retinal signal analysis (Gajendran et al, 2022). Other areas of signal analysis are still to be explored such as variable frequency complex demodulation that provides a high resolution spectral analysis of waveforms (Wang et al, 2006), or a functional data analytical approach where the ERG waveforms could be analyzed as a series of datapoints to identify differences in location scale or shape of the waveforms (Ramsay, 1982;Ramsay and Dalzell, 1991;Ramsay and Silverman, 2005).…”