ArticleSensors (Basel, Switzerland)2023
Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Use of Artificial Intelligence in the Interpretation of Electroretinography (ERG) Studies.International journal of molecular sciences · 2026Pooled it
- A practical introduction to wavelet analysis in electroretinography.Documenta ophthalmologica. Advances in ophthalmology · 2026Review
- Technical note: contour plot visualization of the light adapted electroretinogram using a generalized additive model.Documenta ophthalmologica. Advances in ophthalmology · 2026Article
- Artificial intelligence-based analysis of visual electrophysiological signals for clinical interpretation support.Frontiers in neuroscience · 2026Review
- Synthetic electroretinogram signal generation using a conditional generative adversarial network.Documenta ophthalmologica. Advances in ophthalmology · 2025Article
- Time Series Classification of Autism Spectrum Disorder Using the Light-Adapted Electroretinogram.Bioengineering (Basel, Switzerland) · 2025Article
- Remodeling the light-adapted electroretinogram using a bayesian statistical approach.BMC research notes · 2025Article
- Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial intelligence for detection of retinal toxicity in chloroquine and hydroxychloroquine therapy using multifocal electroretinogram waveforms.Scientific reports · 2024Article
- Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques.Bioengineering (Basel, Switzerland) · 2024Article
- Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations.Journal of ophthalmology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
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.