{"doi":"10.1167/tvst.9.3.12","title":"Artifact Rates for 2D Retinal Nerve Fiber Layer Thickness Versus 3D Retinal Nerve Fiber Layer Volume","abstract":"Purpose: To compare artifact rates in two-dimensional (2D) versus three-dimensional (3D) retinal nerve fiber layer (RNFL) scans using Spectralis optical coherence tomography (OCT) Methods: Thirteen artifact types in 2D and 3D RNFL scans were identified in 106 glaucomatous eyes and 95 normal eyes. Artifact rates were calculated per B-scan and per eye. In 3D volume scans, artifacts were counted only for the 97 B-scans used to calculate RNFL parameters for the 2.5–3.5-mm annulus. 3D RNFL measurements were calculated twice, once before and again after deletion of B-scans with artifacts and subsequent automated interpolation. Results: For 2D scans, artifacts were present in 58.5% of B-scans (62 of 106) in glaucomatous eyes. For 3D scans, a mean of 35.4% of B-scans (34.3 of 97 B-scans per volume scan) contained an artifact in 106 glaucomatous eyes. For 3D data of glaucoma patients, mean global RNFL thickness values were similar before and after interpolation (77.0 ± 11.6 µm vs. 75.1 ± 11.2 µm, respectively; P = 0.23). Fewer clinically significant artifacts were noted in 3D RNFL scans, where only 7.5% of glaucomatous eyes (8 of 106) and 0% of normal eyes (0 of 95) had artifacts, compared to 2D RNFL scans, where 58.5% of glaucomatous eyes (62 of 106) and 14.7% of normal eyes (14 of 95) had artifacts. Conclusions: Compared to 2D RNFL scans, 3D RNFL volume scans less often require manual correction to obtain accurate measurements. Translational Relevance: 3D RNFL volume scans have fewer clinically significant artifacts compared to 2D RNFL thickness scans.","journal":"Translational Vision Science & Technology","year":2020,"id":60718,"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":60,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8777,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":322011,"name":"Firas Jassim","orcid":null,"position":1,"is_corresponding":false},{"id":320694,"name":"Edem Tsikata","orcid":"0000-0001-7671-3224","position":2,"is_corresponding":false},{"id":320695,"name":"Ziad Khoueir","orcid":"0000-0003-1717-1618","position":3,"is_corresponding":false},{"id":320696,"name":"Linda Yi-Chieh Poon","orcid":"0000-0001-6074-5440","position":4,"is_corresponding":false},{"id":322012,"name":"Boy Braaf","orcid":null,"position":5,"is_corresponding":false},{"id":320697,"name":"Benjamin J. Vakoc","orcid":"0000-0002-6623-3515","position":6,"is_corresponding":false},{"id":247839,"name":"Brett E. Bouma","orcid":"0000-0002-4531-2206","position":7,"is_corresponding":false},{"id":320698,"name":"Johannes F. de Boer","orcid":"0000-0003-1253-4950","position":8,"is_corresponding":false},{"id":320699,"name":"Teresa C. Chen","orcid":"0000-0001-5327-2016","position":9,"is_corresponding":false},{"id":320693,"name":"Stephanie Choi","orcid":"0000-0002-9155-7407","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-18T21:09:05.493770Z","pmid":"32714638","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":[]}