Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathology in the retinal vasculature such as microaneurysms (MAs) and vascular leakage. Despite its potential value for diagnosis and disease surveillance, objective quantitative assessment of retinal pathology by UWFA is currently limited because it requires laborious manual segmentation by trained human graders. In this report, we describe a novel fully automated software platform, which segments MAs and leakage areas in native and dewarped UWFA images with retinal vascular disease. Comparison of the algorithm with human grader-generated gold standards demonstrated significant strong correlations for MA and leakage areas (intraclass correlation coefficient (ICC)=0.78–0.87 and ICC=0.70–0.86, respectively, p=2.1×10−7 to 3.5×10–10 and p=7.8×10−6 to 1.3×10–9, respectively). These results suggest the algorithm performs similarly to human graders in MA and leakage segmentation and may be of significant utility in clinical and research settings.
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Contributors All authors contributed to the manuscript and to this project through the analysis of data, gathering of data and revision of the manuscript. JPE and KW both wrote the manuscript and contributed equally to the project and are designated as co-first authors.
Funding NIH/NEIK23-EY022947-01A1 (JPE); Ohio Department of Development TECH-13-059 (JPE, SKS).
Competing interests None declared.
Patient consent This was a retrospective study and did not require consent.
Ethics approval Cleveland Clinic institutional review board.
Provenance and peer review Not commissioned; externally peer reviewed.
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