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- Title
Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence.
- Authors
Pfau, Maximilian; van Dijk, Elon H. C.; van Rijssen, Thomas J.; Schmitz-Valckenberg, Steffen; Holz, Frank G.; Fleckenstein, Monika; Boon, Camiel J. F.
- 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.
- Subjects
ARTIFICIAL intelligence; OPTICAL coherence tomography; RANDOM forest algorithms; PERIMETRY
- Publication
Scientific Reports, 2021, Vol 11, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-021-99977-4