{"doi":"10.1371/journal.pone.0243830","title":"Quantitative assessment of choriocapillaris flow deficits in diabetic retinopathy: A swept-source optical coherence tomography angiography study","abstract":"PURPOSE: To quantitatively assess choriocapillaris (CC) flow deficits in eyes with diabetic retinopathy (DR) using swept-source optical coherence tomography angiography (SS-OCTA). METHODS: Diabetic subjects with different stages of DR and age-matched healthy subjects were recruited and imaged with SS-OCTA. The en face CC blood flow images were generated using previously published and validated algorithms. The percentage of CC flow deficits (FD%) and the mean CC flow deficit size were calculated in a 5-mm-diameter circle centered on the fovea from the 6×6-mm scans. RESULTS: Forty-five diabetic subjects and 27 control subjects were included in the study. The CC FD% in diabetic eyes was on average 1.4-fold greater than in control eyes (12.34±4.14% vs 8.82±2.61%, P < 0.001). The mean CC FD size in diabetic eyes was on average 1.4-fold larger than in control eyes (2151.3± 650.8μm2 vs 1574.4±255.0 μm2, P < 0.001). No significant difference in CC FD% or mean CC FD size was observed between eyes with nonproliferative DR and eyes with proliferative DR (P = 1.000 and P = 1.000, respectively). CONCLUSIONS: CC perfusion in DR can be objectively and quantitatively assessed with FD% and FD size. In the macular region, both CC FD% and CC FD size are increased in eyes with DR. SS-OCTA provides new insights for the investigations of CC perfusion status in diabetes in vivo.","journal":"PLoS ONE","year":2020,"id":104431,"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":24,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8881,"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":395928,"name":"Hao Zhou","orcid":"0000-0003-0068-5102","position":1,"is_corresponding":false},{"id":406321,"name":"Qinqin Zhang","orcid":"0000-0003-3629-1914","position":2,"is_corresponding":false},{"id":382739,"name":"Zhongdi Chu","orcid":"0000-0001-7430-5032","position":3,"is_corresponding":false},{"id":480515,"name":"Lisa C. Olmos de Koo","orcid":"0000-0002-8146-4653","position":4,"is_corresponding":false},{"id":507166,"name":"Jennifer R. Chao","orcid":"0000-0002-6859-5552","position":5,"is_corresponding":false},{"id":364506,"name":"Kasra A. Rezaei","orcid":"0000-0003-4287-3187","position":6,"is_corresponding":false},{"id":507167,"name":"Steven S. Saraf","orcid":"0000-0002-3951-9059","position":7,"is_corresponding":false},{"id":284472,"name":"Ruikang K. Wang","orcid":"0000-0001-5169-8822","position":8,"is_corresponding":false},{"id":395929,"name":"Yining Dai","orcid":"0000-0003-2262-7982","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":null,"created_at":"2026-07-18T22:43:22.755723Z","pmid":"33306736","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":[]}