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

Scientific Reports, vol. 11, pp. 20446

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.

Authors 7

1. Maximilian Pfau

   National Institutes of Health · University of Bonn · National Eye Institute

   Affiliation as printed

   Department of Ophthalmology, University of Bonn, Bonn, Germany

   National Eye Institute, National Institutes of Health, Bethesda, MD, USA

2. Elon H. C. van Dijk Aachen Department of Ophthalmology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Ophthalmology, Leiden University Medical Center, P. O. Box 9600, 2300 RC, Leiden, The Netherlands

3. Thomas J. van Rijssen Aachen Department of Ophthalmology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Ophthalmology, Leiden University Medical Center, P. O. Box 9600, 2300 RC, Leiden, The Netherlands

4. Steffen Schmitz-Valckenberg

   University of Bonn · University of Utah

   Affiliation as printed

   Department of Ophthalmology, University of Bonn, Bonn, Germany

   John A. Moran Eye Center, University of Utah, Utah, USA

5. Frank Gerhard Holz

   University of Bonn

   Affiliation as printed

   Department of Ophthalmology, University of Bonn, Bonn, Germany

6. Monika Fleckenstein

   University of Utah

   Affiliation as printed

   John A. Moran Eye Center, University of Utah, Utah, USA

7. Camiel J. F. Boon corresponding Aachen Department of Ophthalmology, Leiden University Medical Center

   Leiden University Medical Center · Amsterdam University Medical Centers · University of Amsterdam

   Affiliation as printed

   Department of Ophthalmology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands. cjfboon@hotmail.com

   Department of Ophthalmology, Leiden University Medical Center, P. O. Box 9600, 2300 RC, Leiden, The Netherlands. cjfboon@hotmail.com

   Department of Ophthalmology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands