ReviewFrontiers in neuroscience2026
Artificial intelligence-based analysis of visual electrophysiological signals for clinical interpretation support.
Review in Frontiers in neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Visual Pathways Involvement in Friedreich's Ataxia Patients Without Macular Impairment.Journal of clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Visual electrophysiology, including electroretinograms (ERG) and visual evoked potentials (VEP), provides a real-time functional assessment of retinal and post-retinal pathways, complementing structural imaging. Subtypes such as transient, periodic, multifocal, and code-modulated signals probe distinct physiological mechanisms and reveal pathological signatures ranging from photoreceptor dysfunction to cortical pathway impairment. However, interpretation is often challenged by low signal amplitude, noise, and inter-individual variability. Advances in artificial intelligence (AI) enable automated, objective and reproducible analysis, and may improve sensitivity, and scalability in clinical and research environments. We undertook a literature review to identify the potential of automated analysis of brief visual electrophysiology signals to support medical interpretation in ophthalmology. Materials and methods: A review of the 2020-2025 literature was undertaken. Results: AI has been increasingly applied to ERG and VEP signals. These signals encode complex pathophysiological processes. Their features vary widely as they are transient (triggered by a single stimulus), periodic (repeated over time), multifocal (capturing signals from multiple visual field locations), or dependent on specific timing or coding schemes. These properties influence the choice of the most appropriate AI method for analysis. Classical ML methods remain useful for interpretable, feature-based classification of relatively scarce medical data, such as transient/aperiodic VEP and ERG. By modeling latent dynamics, AI can identify subtle or early dysfunction and harmonize interpretation across centers. Conclusion: AI supports reproducible, clinician-independent pipelines for electrophysiology, well-suited to high-volume clinics and large-scale screening. The convergence of standardized acquisition protocols with advanced AI analysis has the potential to deliver more personalized, timely, and objective assessments of visual system integrity in neuro-ophthalmic practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.