Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence

Maximilian Pfau,Frank G Holz,Steffen Schmitz-Valckenberg,Thomas J Van Rijssen,Elon H C Van Dijk,Camiel J F Boon,Monika Fleckenstein

doi:10.1038/s41598-021-99977-4

Abstract

Refined understanding of the association of retinal microstructure with current and future (post-treatment) function in chronic central serous chorioretinopathy (cCSC) may help to identify patients that would benefit most from treatment. In this post-hoc analysis of data from the prospective, randomized PLACE trial (NCT01797861), we aimed to determine the accuracy of AI-based inference of retinal function from retinal morphology in cCSC. Longitudinal spectral-domain optical coherence tomography (SD-OCT) data from 57 eyes of 57 patients from baseline, week 6–8 and month 7–8 post-treatment were segmented using deep-learning software. Fundus-controlled perimetry data were aligned to the SD-OCT data to extract layer thickness and reflectivity values for each test point. Point-wise retinal sensitivity could be inferred with a (leave-one-out) cross-validated mean absolute error (MAE) [95% CI] of 2.93 dB [2.40–3.46] (scenario 1) using random forest regression. With addition of patient-specific baseline data (scenario 2), retinal sensitivity at remaining follow-up visits was estimated even more accurately with a MAE of 1.07 dB [1.06–1.08]. In scenario 3, month 7–8 post-treatment retinal sensitivity was predicted from baseline SD-OCT data with a MAE of 3.38 dB [2.82–3.94]. Our study shows that localized retinal sensitivity can be inferred from retinal structure in cCSC using machine-learning. Especially, prediction of month 7–8 post-treatment sensitivity with consideration of the treatment as explanatory variable constitutes an important step toward personalized treatment decisions in cCSC.

Highlights

Refined understanding of the association of retinal microstructure with current and future function in chronic central serous chorioretinopathy may help to identify patients that would benefit most from treatment
A total of 57 eyes from 57 chronic central serous chorioretinopathy (cCSC) patients (9 female) with a median [IQR] age of 48.79 years [42.80, 52.20] and best-corrected visual acuity of 0.12 LogMAR [0.02, 0.20] at baseline were included in this analysis
The present work evaluated the accuracy of AI-based inference of current and prediction of post-treatment retinal sensitivity from spectral-domain optical coherence tomography (SD-OCT) imaging data in patients with cCSC undergoing treatment and followed over a period of 7–8 months

Summary

Introduction

Refined understanding of the association of retinal microstructure with current and future (posttreatment) function in chronic central serous chorioretinopathy (cCSC) may help to identify patients that would benefit most from treatment. The idea of applying supervised machine-learning to infer retinal function from SD-OCT has been brought forward by multiple groups in the setting of macular telangiectasia type 2 8, choroidal neovascularization and geographic atrophy secondary to age-related macular degeneration (AMD)[9,10], as well as Leber congenital amaurosis (LCA)[11]. This strategy potentially allows to obtain a close surrogate of function—even in patients unfit for psychophysical testing—using ubiquitously available SD-OCT imaging. We have introduced the term “inferred sensitivity’” maps for this a pproach[9,10]

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