Artificial intelligence recognition of non-perfusion areas in fundus fluorescence angiography images
Objective:To explore the ability of artificial intelligence to recognize non-perfusion areas in fun-dus fluorescence angiography(FFA)images.Methods:We collected the results of fundus fluorescence angiogra-phy in our Ophthalmology Department from March 2018 to June 2023.Among 1000 patients,347 whose fluo-rescence angiography images of fundus with non-perfusion areas were screened,and 622 FFA photos were ob-tained with non-perfusion areas.An ophthalmologist with 10 years of clinical experience manually annotated the images using the polygon tool in RectLabel annotation software.The model adopts an overall network structure that combines Vision Transformer and Convolutional Neural Network.Meanwhile,the model adopts a progressive transfer learning strategy and weighted loss function to enhance the recognition ability of the mod-el.Results:A total of 991 areas with non-perfusion were labeled as"positive samples",and a certain number of rectangular areas of the same size were randomly cut as"negative samples"in the areas outside the non-perfusion areas on the fluorescence imaging.Considering the actual incidence rate and clinical proportion of the retinal non-perfusion area,the final sampling ratio of positive and negative samples was 1:9,and the number of negative samples was 8878.The obtained prediction sensitivity was 98.7％,specificity was 95.9％,Jordan index was 94.6％,precision was 73.3％,F-score was 77.9％,and accuracy was 96.9％.Conclusion:The model used in this study has a strong ability to identify non-perfused areas in fundus fluorescence angiogra-phy,with high sensitivity and specificity.
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