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.
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IK and KL contributed equally
Contributors IKK: preparation of the manuscript, collection of data and data analysis; KL: preparation of the manuscript, collection of data and data analysis; JHP: collection of data and data analysis, JB: conception and design of the study, writing manuscript text, preparing figures, collection and assembly of data, data analysis and interpretation, and supervision. WKL: supervision. All authors reviewed the manuscript.
Funding This work was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant no: HI17C2012030018).
Competing interests None declared.
Data sharing statement Data are available upon reasonable request.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.
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