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Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification

PURPOSE: To determine the association between diabetic retinopathy (DR) severity and quantitative retinal vascular features. DESIGN: Retrospective image analysis study. PARTICIPANTS: Eyes with DR and eyes with no posterior segment disease (normal eyes) that had undergone ultra-widefield fluorescein...

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Autores principales: Sevgi, Duriye Damla, Srivastava, Sunil K., Whitney, Jon, O’Connell, Margaret, Kar, Sudeshna Sil, Hu, Ming, Reese, Jamie, Madabhushi, Anant, Ehlers, Justis P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870443/
https://www.ncbi.nlm.nih.gov/pubmed/35224527
http://dx.doi.org/10.1016/j.xops.2021.100049
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author Sevgi, Duriye Damla
Srivastava, Sunil K.
Whitney, Jon
O’Connell, Margaret
Kar, Sudeshna Sil
Hu, Ming
Reese, Jamie
Madabhushi, Anant
Ehlers, Justis P.
author_facet Sevgi, Duriye Damla
Srivastava, Sunil K.
Whitney, Jon
O’Connell, Margaret
Kar, Sudeshna Sil
Hu, Ming
Reese, Jamie
Madabhushi, Anant
Ehlers, Justis P.
author_sort Sevgi, Duriye Damla
collection PubMed
description PURPOSE: To determine the association between diabetic retinopathy (DR) severity and quantitative retinal vascular features. DESIGN: Retrospective image analysis study. PARTICIPANTS: Eyes with DR and eyes with no posterior segment disease (normal eyes) that had undergone ultra-widefield fluorescein angiography (UWFA) with associated color fundus photography. Exclusion criteria were any previous laser photocoagulation, low image quality, intravitreal or periocular pharmacotherapy within 6 months of imaging, and any other significant retinal disease including posterior uveitis, retinal vein occlusion, and choroidal neovascularization. METHODS: The centered early mid-phase UWFA frame that captured the maximum vessel area was selected using automated custom software for each eye. Panretinal and zonal vascular features were extracted using a machine learning algorithm. Eyes with DR were graded for DR severity as mild nonproliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). Parameters of normal eyes were compared with age- and gender-matched patients with DR using the t test. Differences between severity groups were evaluated by the analysis of variance and Kruskal-Wallis tests, generalized linear mixed-effects models, and random forest regression models. MAIN OUTCOME MEASURES: Diabetic retinopathy severity and vascular features (panretinal and zonal vessel area, length and geodesic distance, panretinal area index, tortuosity measures, vascular density measures, and zero vessel density rate). RESULTS: Ninety-seven eyes from 60 patients with DR and 12 normal eyes from 12 patients that underwent UWFA for evaluation of fellow eye pathology had images of sufficient quality to be included in this analysis. The mean age was 60 ± 10 years in DR eyes and 46 ± 17 years in normal eyes. Panretinal vessel area, mean geodesic distance, skewness, and kurtosis of local vessel density was significantly higher in normal eyes compared with the age- and gender-matched eyes with DR (P < 0.05). Zero vessel density rate, skewness of vessel density, and mean mid-peripheral geodesic distance were among the most important features for distinguishing mild NPDR from advanced forms of DR and PDR versus eyes without PDR. CONCLUSIONS: Automated analysis of retinal vasculature demonstrated associations with DR severity and visual and subvisual vascular biomarkers. Further studies are needed to evaluate the clinical significance of these parameters for DR prognosis and therapeutic response.
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spelling pubmed-88704432022-02-24 Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification Sevgi, Duriye Damla Srivastava, Sunil K. Whitney, Jon O’Connell, Margaret Kar, Sudeshna Sil Hu, Ming Reese, Jamie Madabhushi, Anant Ehlers, Justis P. Ophthalmol Sci Original Article PURPOSE: To determine the association between diabetic retinopathy (DR) severity and quantitative retinal vascular features. DESIGN: Retrospective image analysis study. PARTICIPANTS: Eyes with DR and eyes with no posterior segment disease (normal eyes) that had undergone ultra-widefield fluorescein angiography (UWFA) with associated color fundus photography. Exclusion criteria were any previous laser photocoagulation, low image quality, intravitreal or periocular pharmacotherapy within 6 months of imaging, and any other significant retinal disease including posterior uveitis, retinal vein occlusion, and choroidal neovascularization. METHODS: The centered early mid-phase UWFA frame that captured the maximum vessel area was selected using automated custom software for each eye. Panretinal and zonal vascular features were extracted using a machine learning algorithm. Eyes with DR were graded for DR severity as mild nonproliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). Parameters of normal eyes were compared with age- and gender-matched patients with DR using the t test. Differences between severity groups were evaluated by the analysis of variance and Kruskal-Wallis tests, generalized linear mixed-effects models, and random forest regression models. MAIN OUTCOME MEASURES: Diabetic retinopathy severity and vascular features (panretinal and zonal vessel area, length and geodesic distance, panretinal area index, tortuosity measures, vascular density measures, and zero vessel density rate). RESULTS: Ninety-seven eyes from 60 patients with DR and 12 normal eyes from 12 patients that underwent UWFA for evaluation of fellow eye pathology had images of sufficient quality to be included in this analysis. The mean age was 60 ± 10 years in DR eyes and 46 ± 17 years in normal eyes. Panretinal vessel area, mean geodesic distance, skewness, and kurtosis of local vessel density was significantly higher in normal eyes compared with the age- and gender-matched eyes with DR (P < 0.05). Zero vessel density rate, skewness of vessel density, and mean mid-peripheral geodesic distance were among the most important features for distinguishing mild NPDR from advanced forms of DR and PDR versus eyes without PDR. CONCLUSIONS: Automated analysis of retinal vasculature demonstrated associations with DR severity and visual and subvisual vascular biomarkers. Further studies are needed to evaluate the clinical significance of these parameters for DR prognosis and therapeutic response. Elsevier 2021-07-30 /pmc/articles/PMC8870443/ /pubmed/35224527 http://dx.doi.org/10.1016/j.xops.2021.100049 Text en © 2021 by the American Academy of Ophthalmology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Sevgi, Duriye Damla
Srivastava, Sunil K.
Whitney, Jon
O’Connell, Margaret
Kar, Sudeshna Sil
Hu, Ming
Reese, Jamie
Madabhushi, Anant
Ehlers, Justis P.
Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification
title Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification
title_full Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification
title_fullStr Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification
title_full_unstemmed Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification
title_short Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification
title_sort characterization of ultra-widefield angiographic vascular features in diabetic retinopathy with automated severity classification
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8870443/
https://www.ncbi.nlm.nih.gov/pubmed/35224527
http://dx.doi.org/10.1016/j.xops.2021.100049
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