Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations
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 depend...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Ophthalmology |
| Online Access: | http://dx.doi.org/10.1155/2024/1990419 |
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| author | Mikhail Kulyabin Aleksei Zhdanov Andreas Maier Lynne Loh Jose J. Estevez Paul A. Constable |
| author_facet | Mikhail Kulyabin Aleksei Zhdanov Andreas Maier Lynne Loh Jose J. Estevez Paul A. Constable |
| author_sort | Mikhail Kulyabin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-08e130fb5cd04c7fa1634297b8d43d50 |
| institution | DOAJ |
| issn | 2090-0058 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Ophthalmology |
| spelling | doaj-art-08e130fb5cd04c7fa1634297b8d43d502025-08-20T03:16:57ZengWileyJournal of Ophthalmology2090-00582024-01-01202410.1155/2024/1990419Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented PopulationsMikhail Kulyabin0Aleksei Zhdanov1Andreas Maier2Lynne Loh3Jose J. Estevez4Paul A. Constable5Pattern Recognition LabEngineering School of Information TechnologiesPattern Recognition LabFlinders UniversityFlinders UniversityFlinders UniversityVisual 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.http://dx.doi.org/10.1155/2024/1990419 |
| spellingShingle | Mikhail Kulyabin Aleksei Zhdanov Andreas Maier Lynne Loh Jose J. Estevez Paul A. Constable Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations Journal of Ophthalmology |
| title | Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations |
| title_full | Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations |
| title_fullStr | Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations |
| title_full_unstemmed | Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations |
| title_short | Generating Synthetic Light-Adapted Electroretinogram Waveforms Using Artificial Intelligence to Improve Classification of Retinal Conditions in Under-Represented Populations |
| title_sort | generating synthetic light adapted electroretinogram waveforms using artificial intelligence to improve classification of retinal conditions in under represented populations |
| url | http://dx.doi.org/10.1155/2024/1990419 |
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