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

Kulyabin M, Zhdanov A, Maier A, Loh L, Estevez JJ, Constable P (2024)

Publication Type: Journal article, Original article

Publication year: 2024

Journal

Journal of Ophthalmology Hindawi Publishing Corporation

Article Number: 1990419

URI: https://onlinelibrary.wiley.com/doi/10.1155/2024/1990419

DOI: 10.1155/2024/1990419

Open Access Link: https://doi.org/10.1155/2024/1990419

Abstract

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

Authors with CRIS profile

Mikhail Kulyabin Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Flinders University AU Australia (AU) Ural Federal University B.N. Yeltsin RU Russian Federation (RU)

How to cite

APA:

Kulyabin, M., Zhdanov, A., Maier, A., Loh, L., Estevez, J.J., & Constable, P. (2024). Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations. Journal of Ophthalmology. https://doi.org/10.1155/2024/1990419

MLA:

Kulyabin, Mikhail, et al. "Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations." Journal of Ophthalmology (2024).

BibTeX: Download