Attention to the Electroretinogram: Gated Multilayer Perceptron for ASD Classification

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 52352 - 52362

Published: Jan. 1, 2024

The electroretinogram (ERG) is a clinical test that records the retina's electrical response to brief flash of light as waveform signal. Analysis ERG signal offers promising non-invasive method for studying different neurodevelopmental and neurodegenerative disorders. Autism Spectrum Disorder (ASD) condition characterized by poor communication, reduced reciprocal social interaction, restricted and/or repetitive stereotyped behaviors should be detected early possible ensure timely appropriate intervention support individual their family. In this study, we applied gated Multilayer Perceptron (gMLP) light-adapted classification an effective alternative Transformers. first reported application model ASD which consisted basic multilayer perceptrons, with fewer parameters than We compared performance time-series models on ASD-Control dataset found superiority gMLP in accuracy was best at 89.7% supports use based recordings involving case-control comparisons.

Language: Английский

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Generating Synthetic Light‐Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under‐Represented Populations

Journal of Ophthalmology, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Visual electrophysiology is often used clinically to determine the functional changes associated with retinal or neurological conditions. The full‐field flash electroretinogram (ERG) assesses global contribution of outer and inner layers initiated by rods cone pathways depending on state adaptation. Within clinical centers, reference normative data are compare cases that may be rare underpowered within a specific demographic. To bolster either dataset case dataset, application synthetic ERG waveforms offer benefits disease classification case‐control studies. In this study as proof concept, artificial intelligence (AI) generate signals using generative adversarial networks deployed upscale male participants an ISCEV containing 68 participants, from right left eye. Random forest classifiers further improved for sex group balanced accuracy 0.72–0.83 added waveforms. This first demonstrate generation improve machine learning modelling

Language: Английский

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Synthetic electroretinogram signal generation using a conditional generative adversarial network

Documenta Ophthalmologica, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Abstract Purpose The electroretinogram (ERG) records the functional response of retina. In some neurological conditions, ERG waveform may be altered and could support biomarker discovery. heterogeneous or rare populations, where either large data sets availability a challenge, synthetic signals with Artificial Intelligence (AI) help to mitigate against these factors classification models. Methods This approach was tested using publicly available dataset real ERGs, n = 560 (ASD) 498 (Control) recorded at 9 different flash strengths from 18 ASD (mean age 12.2 ± 2.7 years) 31 Controls 11.8 3.3 that were augmented waveforms, generated through Conditional Generative Adversarial Network. Two deep learning models used classify groups only combined ERGs. One Time Series Transformer (with waveforms in their original form) second Visual model utilizing images wavelets derived Continuous Wavelet Transform Model performance classifying evaluated Balanced Accuracy (BA) as main outcome measure. Results BA improved 0.756 0.879 when ERGs included across all recordings for training Transformer. also achieved best 0.89 single strength 0.95 log cd s m −2 . Conclusions supports application AI improve group recordings.

Language: Английский

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Enhancing the diagnostic potential of electroretinography in Parkinson's disease: A review of protocol and cohort criteria

Journal of Parkinson s Disease, Journal Year: 2025, Volume and Issue: unknown

Published: April 29, 2025

Electroretinography has emerged as a promising tool for identifying retinal functional anomalies in major psychiatric and neurodevelopmental disorders, such schizophrenia, depressive disorder, bipolar autism spectrum positioning it potential biomarker of monoaminergic dysfunction. However, despite its potential, electroretinography studies Parkinson's disease (PD) over the past decades have been inconsistent, largely due to variations research methodologies. These limitations diminish hinder association between electrophysiological responses PD neuropathology. To address this challenge, review examines most relevant sources data variability reduced reproducibility aimed at detecting signature characteristic PD. We propose consolidation four key protocol factors five cohort criteria enhance diagnostic accuracy research. As protocols are adapted from their clinical origins purposes, we argue that careful attention must be given electrode type placement, well like age, sex, duration severity, medication intake, conditions, comorbidities selection ensure reproducible results. Suggesting inconsistencies these areas may explain reported results contribute lack consensus on which parameters comprise PD, ultimately offer recommendations improve utility techniques early biomarkers

Language: Английский

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Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning

Bioengineering, Journal Year: 2024, Volume and Issue: 12(1), P. 15 - 15

Published: Dec. 28, 2024

Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity (ADHD). In series ERGs collected in ASD (n = 77), ADHD 43), + 21), control 137) groups, this analysis explores the use machine learning feature selection techniques to improve classification these clinically defined groups. Standard time domain signal features were evaluated different models. For classification, balanced accuracy (BA) 0.87 was achieved for male participants. ADHD, BA 0.84 female When three-group model (ASD, control) lower, at 0.70, fell further 0.53 when all groups included control). The findings support role ERG establishing broad two-group but model's performance depends upon sex is limited multiple classes are modeling.

Language: Английский

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Exploring autism via the retina: Comparative insights in children with autism spectrum disorder and typical development

Autism Research, Journal Year: 2024, Volume and Issue: 17(8), P. 1520 - 1533

Published: July 29, 2024

Abstract Autism spectrum disorder (ASD) is a widely recognized neurodevelopmental disorder, yet the identification of reliable imaging biomarkers for its early diagnosis remains challenge. Considering specific manifestations ASD in eyes and interconnectivity between brain eyes, this study investigates through lens retinal analysis. We specifically examined differences macular region retina using optical coherence tomography (OCT)/optical angiography (OCTA) images children diagnosed with those typical development (TD). Our findings present potential novel characteristics ASD: thickness ellipsoid zone (EZ) cone photoreceptors was significantly increased ASD; large‐caliber arteriovenous inner reduced these changes EZ were more significant left eye than right eye. These observations photoreceptor alterations, vascular function changes, lateralization phenomena warrant further investigation, we hope that work can advance interdisciplinary understanding ASD.

Language: Английский

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