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Amani Al-Tarouti, Grant Michael Comer, Pavan S Angadi, Christopher Ranella, Nathan Patel, Daniel Albertus, Maxwell Stem, Matthew Johnson-Roberson, Thiran Jayasundera; Automated Detection of Fluorescein Leakage in Diabetic Macular Edema. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4832.
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© ARVO (1962-2015); The Authors (2016-present)
To determine the accuracy of a novel method of quantitative image analysis for fluorescein angiography (FA) that demonstrates clinical utility in identifying and following treatment efficacy of fluorescein leakage extravasation in diabetic macular edema (DME).
This proof of concept study obtained a small FA image database (88) of diabetic macular edema eyes, before and 6 months after treatment with intravitreal angiogenesis inhibitors. Eight images were chosen. Two of these images had no leakage. The baseline and 6 months post-treatment images were analyzed for quantification of area change based on pixel area difference between early (25-30 seconds) and late (after 2-3 minutes) FA images. For each image, areas of fluorescein leakage extravasation were outlined and graded according to the ETDRS grading scheme by two human graders (retina specialists) utilizing an interactive pen display thus generating quantifiable values across the macula. A comparison of the generated signatures was performed by calculating histogram information of the pixels in the outlined regions, producing a single quantitative value of the area change ( pixel difference from early to late phase ); the Index of Retinal Leakage (IRL). Inter-grader variability was calculated between the indices outlined by the human graders. The IRL was compared to a (texture based feature vector classification) algorithm generated value and calculated as an IRL as well, thus quantifying leakage area change.
The area change generated by the vector algorithm were similar to that observed by the specialists’ interactive grading. The algorithm was able to demonstrate lack of leakage as well as the human graders by producing zero area change. There was less variability demonstrated by the algorithm compared to the human grader variability.
The IRL generated by the new algorithm exhibited acceptable inter-grader variability for effective quantification of changes in FA images due to fluorescein leakage inDME extravasation and may serve as a new outcome measure tool to in quantify disease quantifying disease progression and monitoring response to treatment. Further investigation of the algorithm will be done to demonstrate consistency and reproducibility of results to establish an objective leakage assessment process in DME.
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