{"doi":"10.1167/tvst.12.11.14","title":"Development of an Automated Electroretinography Analysis Approach","abstract":"Purpose: Electroretinography (ERG) is used to assess retinal function in ophthalmology clinics and animal models of ocular disease; however, analyzing ERG waveforms can be a time-intensive process with interobserver variability. We developed ERGAssist, an automated approach, to perform non-subjective and repeatable feature identification (\"marking\") of the ERG waveform. Methods: The automated approach denoised the recorded waveforms and then located the b-wave after applying a lowpass filter. If an a-wave was present, the lowpass filter wave was also used to help locate the a-wave, which was considered the initial large negative response after the flash stimuli. Oscillatory potentials (OPs) were found using a bandpass filter on the denoised waveform. We used two cohorts. One was a Coherence cohort that consisted of ERGs with eight dark-adapted and three light-adapted stimuli in Brown Norway rats (-6 to 1.5 log cd·s/m2). The Verification cohort consisted of control and diabetic (DM) Long Evans rats. We examined retinal function using a five-step dark-adapted protocol (-3 to 1.9 log cd·s/m2). Results: ERGAssist showed a strong correlation with manual markings of ERG features in our Coherence dataset, including the amplitudes (a-wave: r2 = 0.99; b-wave: r2 = 0.99; OP: r2 = 0.92) and implicit times (a-wave: r2 = 0.96; b-wave: r2 = 0.90; OP: r2 = 0.96). In the Verification cohort, both approaches detected differences between control and DM animals and found longer OP implicit times (P < 0.0001) in DM animals. Conclusions: These results provide verification of ERGAssist to identify features of the full-field ERG. Translational Relevance: This ERG analysis approach can increase the rigor of basic science studies designed to investigate retinal function using full-field ERG. To aid the community, we have developed an open-source graphical user interface (GUI) implementing the methods presented.","journal":"Translational Vision Science & Technology","year":2023,"id":356546,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9441,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":381103,"name":"Rachael S Allen","orcid":"0000-0002-7216-6646","position":1,"is_corresponding":false},{"id":1106136,"name":"Kyle Chesler","orcid":null,"position":2,"is_corresponding":false},{"id":288916,"name":"Machelle T. Pardue","orcid":"0000-0001-8738-6839","position":3,"is_corresponding":false},{"id":336415,"name":"Andrew Feola","orcid":"0000-0002-7914-989X","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-19T01:13:30.556819Z","pmid":"37943551","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}