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Bioimpacts. 2015;5(4): 183-190. doi: 10.15171/bi.2015.27
PMID: 26929922        PMCID: PMC4769788

Original Research

Detection of retinal capillary nonperfusion in fundus fluorescein angiogram of diabetic retinopathy

Seyed Hossein Rasta 1,2 * , Shima Nikfarjam 1, Alireza Javadzadeh 3

Cited by CrossRef: 31

1- Ra H, Park J, Baek J, Baek J. Relationships among Retinal Nonperfusion, Neovascularization, and Vascular Endothelial Growth Factor Levels in Quiescent Proliferative Diabetic Retinopathy. JCM. 2020;9(5):1462 [Crossref]
2- Inanc M, Tekin K, Kiziltoprak H, Ozalkak S, Doguizi S, Aycan Z. Changes in Retinal Microcirculation Precede the Clinical Onset of Diabetic Retinopathy in Children With Type 1 Diabetes Mellitus. American Journal of Ophthalmology. 2019;207:37 [Crossref]
3- Lee P, Ra H, Baek J. Automated segmentation of ultra-widefield fluorescein angiography of diabetic retinopathy using deep learning. Br J Ophthalmol. 2023;107(12):1859 [Crossref]
4- Shiromani S, Pattathil N, Sadeghi E, Choudhry N, Chhablani J. Wide field imaging biomarkers: A different perspective. 2024;14(4):510 [Crossref]
5- Elahi R, Nazari M, Mohammadi V, Esmaeilzadeh K, Esmaeilzadeh A. IL-17 in type II diabetes mellitus (T2DM) immunopathogenesis and complications; molecular approaches. Molecular Immunology. 2024;171:66 [Crossref]
6- Fan W, Uji A, Wang K, Falavarjani K, Wykoff C, Brown D, Van Hemert J, Sagong M, Sadda S, Ip M. SEVERITY OF DIABETIC MACULAR EDEMA CORRELATES WITH RETINAL VASCULAR BED AREA ON ULTRA-WIDE FIELD FLUORESCEIN ANGIOGRAPHY. 2020;40(6):1029 [Crossref]
7- Markan A, Agarwal A, Arora A, Bazgain K, Rana V, Gupta V. Novel imaging biomarkers in diabetic retinopathy and diabetic macular edema. Ophthalmol Eye Dis. 2020;12 [Crossref]
8- Oganov A, Seddon I, Jabbehdari S, Uner O, Fonoudi H, Yazdanpanah G, Outani O, Arevalo J. Artificial intelligence in retinal image analysis: Development, advances, and challenges. Survey of Ophthalmology. 2023;68(5):905 [Crossref]
9- Anegondi N, Chidambara L, Bhanushali D, Gadde S, Yadav N, Sinha Roy A. An automated framework to quantify areas of regional ischemia in retinal vascular diseases with OCT angiography. Journal of Biophotonics. 2018;11(2) [Crossref]
10- Özata K, Atum M, Çelik E, Doğan E, Alagöz G. Efficacy of intravitreal dexamethasone implant in persistent diabetic macular edema after primary treatment with intravitreal ranibizumab. Journal of Current Ophthalmology. 2019;31(3):281 [Crossref]
11- Lindstrom S, Sigurdardottir S, Zapadka T, Tang J, Liu H, Taylor B, Smith D, Lee C, DeAngelis J, Kern T, Taylor P. Diabetes induces IL-17A-Act1-FADD-dependent retinal endothelial cell death and capillary degeneration. Journal of Diabetes and its Complications. 2019;33(9):668 [Crossref]
12- Reddy N, Venkatesh R, Agrawal S, Mishra P, Chhablani J. Chimney leak in proliferative diabetic retinopathy. 2022;2(1):292 [Crossref]
13- Omari A, Cooper C, Desjarlais E, Cook M, Abalem M, Andrews C, Joltikov K, Khan R, Chen A, DeOrio A, Gardner T, Paulus Y, Jayasundera K. Grid-Based Software for Quantification of Diabetic Retinal Nonperfusion on Ultra-Widefield Fluorescein Angiography. Diagnostics. 2025;15(7):875 [Crossref]
14- Masayoshi K, Katada Y, Ozawa N, Ibuki M, Negishi K, Kurihara T. Automatic segmentation of non-perfusion area from fluorescein angiography using deep learning with uncertainty estimation. Informatics in Medicine Unlocked. 2022;32:101060 [Crossref]
15- Wang F, Saraf S, Zhang Q, Wang R, Rezaei K. Ultra-Widefield Protocol Enhances Automated Classification of Diabetic Retinopathy Severity with OCT Angiography. Ophthalmology Retina. 2020;4(4):415 [Crossref]
16- Nunez do Rio J, Sen P, Rasheed R, Bagchi A, Nicholson L, Dubis A, Bergeles C, Sivaprasad S. Deep Learning-Based Segmentation and Quantification of Retinal Capillary Non-Perfusion on Ultra-Wide-Field Retinal Fluorescein Angiography. JCM. 2020;9(8):2537 [Crossref]
17- Jin K, Pan X, You K, Wu J, Liu Z, Cao J, Lou L, Xu Y, Su Z, Yao K, Ye J. Automatic detection of non-perfusion areas in diabetic macular edema from fundus fluorescein angiography for decision making using deep learning. Sci Rep. 2020;10(1) [Crossref]
18- Więcławek W, Danch-Wierzchowska M, Rudzki M, Sędziak-Marcinek B, Teper S. Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features. Sensors. 2021;22(1):12 [Crossref]
19- Huang Z, Qiu K, Yi J, Lin H, Zheng D, Huang D, Zhang G, Chen H, Zheng J, Wang Y, Fang D, Chen W. Diabetic retinopathy with extensively large area of capillary non-perfusion: characteristics and treatment outcomes. BMC Ophthalmol. 2022;22(1) [Crossref]
20- Mohite A, Perais J, McCullough P, Lois N. Retinal Ischaemia in Diabetic Retinopathy: Understanding and Overcoming a Therapeutic Challenge. JCM. 2023;12(6):2406 [Crossref]
21- Tang Z, Zhang X, Yang G, Zhang G, Gong Y, Zhao K, Xie J, Hou J, Hou J, Sun B, Wang Z. Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks. Medical Physics. 2021;48(2):648 [Crossref]
22- K.M. V, Tummala V, Sangaraju Y, Reddy M, Kumar P, Mayya V, Kulkarni U, Bhandary S, S. S. FFA-Lens: Lesion detection tool for chronic ocular diseases in Fluorescein angiography images. SoftwareX. 2024;26:101646 [Crossref]
23- Gao Z, Jin K, Yan Y, Liu X, Shi Y, Ge Y, Pan X, Lu Y, Wu J, Wang Y, Ye J. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning. Graefes Arch Clin Exp Ophthalmol. 2022;260(5):1663 [Crossref]
24- Use of OCTA, FA, and Ultra-Widefield Imaging in Quantifying Retinal Ischemia: A Review. Asia Pac J Ophthalmol (Phila). 2019; [Crossref]
25- Xiang D, Yan S, Guan Y, Cai M, Li Z, Liu H, Chen X, Tian B. Semi-Supervised Dual Stream Segmentation Network for Fundus Lesion Segmentation. IEEE Trans Med Imaging. 2023;42(3):713 [Crossref]
26- Wang X, Ji Z, Ma X, Zhang Z, Yi Z, Zheng H, Fan W, Chen C, Hu Y. Automated Grading of Diabetic Retinopathy with Ultra-Widefield Fluorescein Angiography and Deep Learning. Journal of Diabetes Research. 2021;2021:1 [Crossref]
27- Pushparani D, Varalakshmi J, Roobini K, Hamshapriya P, Livitha A. Diabetic Retinopathy-A Review. CDR. 2025;21(7) [Crossref]
28- Zapadka T, Lindstrom S, Taylor B, Lee C, Tang J, Taylor Z, Howell S, Taylor P. RORγt Inhibitor-SR1001 Halts Retinal Inflammation, Capillary Degeneration, and the Progression of Diabetic Retinopathy. IJMS. 2020;21(10):3547 [Crossref]
29- Mota R, Morgan S, Bahnson E. Diabetic Vasculopathy: Macro and Microvascular Injury. Curr Pathobiol Rep. 2020;8(1):1 [Crossref]
30- Mohammadi F, Esmaeili M, Javadzadeh A, Tabar H, Rasta S. The computer based method to diabetic retinopathy assessment in retinal images: a review. Electron J Gen Med. 2019;16(2):em114 [Crossref]
31- Feng W, Wang B, Song D, Li M, Chen A, Wang J, Lin S, Zhao Y, Wang B, Ge Z, Xu S, Hu Y. Development and evaluation of a deep learning model for automatic segmentation of non-perfusion area in fundus fluorescein angiography. J Big Data. 2024;11(1) [Crossref]

As a peer-reviewed international open-access journal, BioImpacts publishes articles on basic and translational aspects of pharmaceutical and biomedical sciences. 
Acceptance rate (2024): 20% 
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