Classification of pachychoroid disease on ultrawide-field indocyanine green angiography using auto-machine learning platform.

Aims: Automatic identification of pachychoroid maybe used as an adjunctive method to confirm the condition and be of help in treatment for macular diseases. This study investigated the feasibility of classifying pachychoroid disease on ultra-widefield indocyanine green angiography (UWF ICGA) images using an automated machine-learning platform.

Methods: Two models were trained with a set including 783 UWF ICGA images of patients with pachychoroid (n=376) and non-pachychoroid (n=349) diseases using the AutoML Vision (Google). Pachychoroid was confirmed using quantitative and qualitative choroidal morphology on multimodal imaging by two retina specialists. Model 1 used the original and Model 2 used images of the left eye horizontally flipped to the orientation of the right eye to increase accuracy by equalising the mirror image of the right eye and left eye. The performances were compared with those of human experts.

Results: In total, 284, 279 and 220 images of central serous chorioretinopathy, polypoidal choroidal vasculopathy and neovascular age-related maculopathy were included. The precision and recall were 87.84% and 87.84% for Model 1 and 89.19% and 89.19% for Model 2, which were comparable to the results of the retinal specialists (90.91% and 95.24%) and superior to those of ophthalmic residents (68.18% and 92.50%).

Conclusions: Auto machine-learning platform can be used in the classification of pachychoroid on UWF ICGA images after careful consideration for pachychoroid definition and limitation of the platform including unstable performance on the medical image.