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Title:

Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy.

Document type:
Journal Article
Author(s):
Fayed, Alaa E; Menten, Martin J; Kreitner, Linus; Paetzold, Johannes C; Rueckert, Daniel; Bassily, Sherry M; Fikry, Ramy R; Hagag, Ahmed M; Sivaprasad, Sobha
Abstract:
OBJECTIVES: To study the changes in vessel densities (VD) stratified by vessel diameter in the retinal superficial and deep vascular complexes (SVC/DVC) using optical coherence tomography angiography (OCTA) images obtained from people with diabetes and age-matched healthy controls. METHODS: We quantified the VD based on vessel diameter categorized as <10, 10-20 and >20 μm in the SVC/DVC obtained on 3 × 3 mm2 OCTA scans using a deep learning-based segmentation and vascular graph extraction tool in people with diabetes and age-matched healthy controls. RESULTS: OCTA images obtained from 854 eyes of 854 subjects were divided into 5 groups: healthy controls (n = 555); people with diabetes with no diabetic retinopathy (DR, n = 90), mild and moderate non-proliferative DR (NPDR) (n = 96), severe NPDR (n = 42) and proliferative DR (PDR) (n = 71). Both SVC and DVC showed significant decrease in VD with increasing DR severity (p < 0.001). The largest difference was observed in the <10 μm vessels of the SVC between healthy controls and no DR (13.9% lower in no DR, p < 0.001). Progressive decrease in <10 μm vessels of the SVC and DVC was seen with increasing DR severity (p < 0.001). However, 10-20 μm vessels only showed decline in the DVC, but not the SVC (p < 0.001) and there was no change observed in the >20 μm vessels in either plexus. CONCLUSIONS: Our findings suggest that OCTA is able to demonstrate a distinct vulnerability of the smallest retinal vessels in both plexuses that worsens with increasing severity of DR.
Journal title abbreviation:
Eye
Year:
2024
Journal volume:
38
Journal issue:
9
Pages contribution:
1762-1769
Fulltext / DOI:
doi:10.1038/s41433-024-03021-4
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/38514853
Print-ISSN:
0950-222X
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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