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

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: Maximilian Pfau, Elon H C van Dijk, Thomas J van Rijssen, Steffen Schmitz-Valckenberg, Frank G Holz, Monika Fleckenstein, Camiel J F Boon
• Journal: Scientific reports (Sci Rep) Vol. 11 Issue 1 Pg. 20446 (10 14 2021) ISSN: 2045-2322 [Electronic] England
• PMID: 34650220 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: © 2021. The Author(s).

Topics

• Adult
• Artificial Intelligence
• Central Serous Chorioretinopathy (diagnostic imaging, physiopathology, therapy)
• Female
• Fundus Oculi
• Humans
• Male
• Middle Aged
• Retina (diagnostic imaging, physiology)
• Tomography, Optical Coherence
• Treatment Outcome