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

Application of Artificial Intelligence in the Diagnosis, Follow-Up and Prediction of Treatment of Ophthalmic Diseases

PURPOSE

To describe the application of artificial intelligence (AI) in ophthalmic diseases and its possible future directions.

METHODS

A retrospective review of the literature from PubMed, Web of Science, and Embase databases (2019-2024).

RESULTS

AI assists in cataract diagnosis, classification, preoperative lens calculation, surgical risk, postoperative vision prediction, and follow-up. For glaucoma, AI enhances early diagnosis, progression prediction, and surgical risk assessment. It detects diabetic retinopathy early and predicts treatment effects for diabetic macular edema. AI analyzes fundus images for age-related macular degeneration (AMD) diagnosis and risk prediction. Additionally, AI quantifies and grades vitreous opacities in uveitis. For retinopathy of prematurity, AI facilitates disease classification, predicting disease occurrence and severity. Recently, AI also predicts systemic diseases by analyzing fundus vascular changes.

CONCLUSIONS

AI has been extensively used in diagnosing, following up, and predicting treatment outcomes for common blinding eye diseases. In addition, it also has a unique role in the prediction of systemic diseases.

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

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

Artificial intelligence for detection of retinal toxicity in chloroquine and hydroxychloroquine therapy using multifocal electroretinogram waveforms

Chloroquine and hydroxychloroquine, while effective in rheumatology, pose risks of retinal toxicity, necessitating regular screening to prevent visual disability. The gold standard for screening includes retinal imaging and automated perimetry, with multifocal electroretinography (mfERG) being a recognized but less accessible method. This study explores the efficacy of Artificial Intelligence (AI) algorithms for detecting retinal damage in patients undergoing (hydroxy-)chloroquine therapy. We analyze the mfERG data, comparing the performance of AI models that utilize raw mfERG time-series signals against models using conventional waveform parameters. Our classification models aimed to identify maculopathy, and regression models were developed to predict perimetric sensitivity. The findings reveal that while regression models were more adept at predicting non-disease-related variation, AI-based models, particularly those utilizing full mfERG traces, demonstrated superior predictive power for disease-related changes compared to linear models. This indicates a significant potential to improve diagnostic capabilities, although the unbalanced nature of the dataset may limit some applications.

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer

The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of retinal diagnostics and treatment control. This study aims to improve the classification accuracy of the previous work using the combination of three optimal mother wavelet functions. We apply Continuous Wavelet Transform (CWT) on a dataset of mixed pediatric and adult ERG signals and show the possibility of simultaneous analysis of the signals. The modern Visual Transformer-based architectures are tested on a time-frequency representation of the signals. The method provides 88% classification accuracy for Maximum 2.0 ERG, 85% for Scotopic 2.0, and 91% for Photopic 2.0 protocols, which on average improves the result by 7.6% compared to previous work.

The Value of Electroretinography in Identifying Candidate Genes for Inherited Retinal Dystrophies: A Diagnostic Guide

Inherited retinal dystrophies (IRDs) are a group of heterogeneous diseases caused by genetic mutations that specifically affect the function of the rod, cone, or bipolar cells in the retina. Electroretinography (ERG) is a diagnostic tool that measures the electrical activity of the retina in response to light stimuli, and it can help to determine the function of these cells. A normal ERG response consists of two waves, the a-wave and the b-wave, which reflect the activity of the photoreceptor cells and the bipolar and Muller cells, respectively. Despite the growing availability of next-generation sequencing (NGS) technology, identifying the precise genetic mutation causing an IRD can be challenging and costly. However, certain types of IRDs present with unique ERG features that can help guide genetic testing. By combining these ERG findings with other clinical information, such as on family history and retinal imaging, physicians can effectively narrow down the list of candidate genes to be sequenced, thereby reducing the cost of genetic testing. This review article focuses on certain types of IRDs with unique ERG features. We will discuss the pathophysiology and clinical presentation of, and ERG findings on, these disorders, emphasizing the unique role ERG plays in their diagnosis and genetic testing.

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

The retina and brain share similar neurochemistry and neurodevelopmental origins, with the retina, often viewed as a "window to the brain." With retinal measures of structure and function becoming easier to obtain in clinical populations there is a growing interest in using retinal findings as potential biomarkers for disorders affecting the central nervous system. Functional retinal biomarkers, such as the electroretinogram, show promise in neurological disorders, despite having limitations imposed by the existence of overlapping genetic markers, clinical traits or the effects of medications that may reduce their specificity in some conditions. This narrative review summarizes the principal functional retinal findings in central nervous system disorders and related mouse models and provides a background to the main excitatory and inhibitory retinal neurotransmitters that have been implicated to explain the visual electrophysiological findings. These changes in retinal neurochemistry may contribute to our understanding of these conditions based on the findings of retinal electrophysiological tests such as the flash, pattern, multifocal electroretinograms, and electro-oculogram. It is likely that future applications of signal analysis and machine learning algorithms will offer new insights into the pathophysiology, classification, and progression of these clinical disorders including autism, attention deficit/hyperactivity disorder, bipolar disorder, schizophrenia, depression, Parkinson's, and Alzheimer's disease. New clinical applications of visual electrophysiology to this field may lead to earlier, more accurate diagnoses and better targeted therapeutic interventions benefiting individual patients and clinicians managing these individuals and their families.

Visual electrophysiology and "the potential of the potentials"

Visual electrophysiology affords direct, quantitative, objective assessment of visual pathway function at different levels, and thus yields information complementary to, and not necessarily obtainable from, imaging or psychophysical testing. The tests available, and their indications, have evolved, with many advances, both in technology and in our understanding of the neural basis of the waveforms, now facilitating more precise evaluation of physiology and pathophysiology. After summarising the visual pathway and current standard clinical testing methods, this review discusses, non-exhaustively, several developments, focusing particularly on human electroretinogram recordings. These include new devices (portable, non-mydiatric, multimodal), novel testing protocols (including those aiming to separate rod-driven and cone-driven responses, and to monitor retinal adaptation), and developments in methods of analysis, including use of modelling and machine learning. It is likely that several tests will become more accessible and useful in both clinical and research settings. In future, these methods will further aid our understanding of common and rare eye disease, will help in assessing novel therapies, and will potentially yield information relevant to neurological and neuro-psychiatric conditions.

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

Background

To evaluate the electroretinogram waveform in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) using a discrete wavelet transform (DWT) approach.

Methods

A total of 55 ASD, 15 ADHD and 156 control individuals took part in this study. Full field light-adapted electroretinograms (ERGs) were recorded using a Troland protocol, accounting for pupil size, with five flash strengths ranging from –0.12 to 1.20 log photopic cd.s.m^–2. A DWT analysis was performed using the Haar wavelet on the waveforms to examine the energy within the time windows of the a- and b-waves and the oscillatory potentials (OPs) which yielded six DWT coefficients related to these parameters. The central frequency bands were from 20–160 Hz relating to the a-wave, b-wave and OPs represented by the coefficients: a20, a40, b20, b40, op80, and op160, respectively. In addition, the b-wave amplitude and percentage energy contribution of the OPs (%OPs) in the total ERG broadband energy was evaluated.

Results

There were significant group differences (p < 0.001) in the coefficients corresponding to energies in the b-wave (b20, b40) and OPs (op80 and op160) as well as the b-wave amplitude. Notable differences between the ADHD and control groups were found in the b20 and b40 coefficients. In contrast, the greatest differences between the ASD and control group were found in the op80 and op160 coefficients. The b-wave amplitude showed both ASD and ADHD significant group differences from the control participants, for flash strengths greater than 0.4 log photopic cd.s.m^–2 (p < 0.001).

Conclusion

This methodological approach may provide insights about neuronal activity in studies investigating group differences where retinal signaling may be altered through neurodevelopment or neurodegenerative conditions. However, further work will be required to determine if retinal signal analysis can offer a classification model for neurodevelopmental conditions in which there is a co-occurrence such as ASD and ADHD.