{"doi":"10.1101/2023.02.25.529586","title":"AIM (Angular Indication Measurement)- Visual Acuity: An adaptive, self-administered, and generalizable vision assessment method used to measure visual acuity","abstract":"This proof-of-concept study introduces Angular Indication Measurement and applies it to VA (AIM-VA). First, we compared the ability of AIM-VA and ETDRS to detect defocus and astigmatic blur in 22 normally-sighted adults. Spherical and cylindrical lenses (±0.00D, +0.25D, +0.50D, +0.75D, +1.00D, +2.00D and +0.50D, +1.00D, +2.00D each at 0°, 90°, 135°, respectively) in the dominant eye induced blur. Second, we compared repeatability over two tests of AIM-VA and ETDRS. A 2-way-ANOVA showed a main effect for defocus-blur and test with no interaction. A 3-way-ANOVA for the astigmatism experiment revealed main effects for test type, blur, and direction and with no interactions. Planned multiple comparisons showed AIM had greater astigmatic-induced VA loss than ETDRS. Bland-Altman plots showed small bias and no systematic learning effect for either test type and improved repeatability with >2 adaptive steps for AIM-VA. AIM-VA's ability to detect defocus was comparable with that of an ETDRS letter chart and showed greater sensitivity to astigmatic blur, and AIM-VA's repeatability is comparable with ETDRS when using 2 or more adaptive steps. AIM's self-administered orientation judgment approach is generalizable to interrogate other visual functions, e.g., contrast, color, motion, stereo-vision.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":403336,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9517,"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":589384,"name":"Jingyi He","orcid":"0000-0001-6100-6367","position":1,"is_corresponding":false},{"id":1181134,"name":"Jay Bijesh Shah","orcid":null,"position":2,"is_corresponding":false},{"id":1181135,"name":"Nicolas Aycardi","orcid":null,"position":3,"is_corresponding":false},{"id":1123744,"name":"Michelle L. Freeman","orcid":null,"position":4,"is_corresponding":false},{"id":342310,"name":"Peter J. Bex","orcid":"0000-0001-7561-7695","position":5,"is_corresponding":false},{"id":927008,"name":"Jan Skerswetat","orcid":"0000-0002-2470-8404","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T01:20:36.280647Z","pmid":"36909639","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":[]}