Automatic detection of parapapillary atrophy and its association with children myopia

Background and objective: To develop an automatic parapapillary atrophy (PPA) detection algorithm in retinal fundus images and discuss the association between PPA and myopia to facilitate diagnosis and prediction of children myopia. Methods: The proposed algorithm consists of PPA identification and segmentation, which are evaluated by comparing with ophthalmologist's annotation. The association between PPA parameters and myopia is analyzed via Spearman correlation. Results: The accuracy of PPA identification reaches 90.78%. The F1-score of PPA segmentation is 0.67, and the Pearson correlation between the automatic measurement and ground truths for the area of PPA (A_PPA), the ratio (μ) of A_PPA to the area of optic disc (OD) and the maximal width of PPA (W) are 0.74, 0.60, and 0.69 (all p < 0.001). All these parameter changes are significantly correlated with the change of ratio of axial length to corneal curvature (ΔALCC), spherical equivalent (ΔSE), and axial length (ΔAL) (all p < 0.01), in which the highest association is 0.75 between ΔW (the change of W) and ΔALCC. Conclusions: The proposed algorithm can provide accurate PPA measurement. Strong association between the changes of PPA and the progress of children myopia are observed and the width of PPA has the best association among three PPA parameters.