TY - JOUR
T1 - Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence
AU - Pfau, Maximilian
AU - van Dijk, Elon H. C.
AU - van Rijssen, Thomas J.
AU - Schmitz-Valckenberg, Steffen
AU - Holz, Frank G.
AU - Fleckenstein, Monika
AU - Boon, Camiel J. F.
N1 - Funding Information: Supported by Stichting Macula Fonds; Retina Nederland Onderzoek Fonds; Stichting Blinden-Penning; Alge-mene Nederlandse Vereniging ter Voorkoming van Blindheid; Landelijke Stichting voor Blinden en Slechtz-ienden, which contributed through UitZicht (Delft, the Netherlands); Rotterdamse Stichting Blindenbelangen (Rotterdam, the Netherlands); Stichting Leids Oogheelkundig Ondersteuningsfonds (Leiden, the Netherlands); Haagse Stichting Blindenhulp (The Hague, the Netherlands); Stichting Ooglijders (Rotterdam, the Netherlands); and the Oxford NIHR Biomedical Research Center (Oxford, United Kingdom); the Gisela Thier Fellowship of Leiden University, Leiden, the Netherlands (C.J.F.B.); and the Netherlands Organisation for Scientific Research (VENI grant to C.J.F.B.); in part by an Unrestricted Grant from Research to Prevent Blindness, New York, NY, to the Department of Ophthalmology & Visual Sciences, University of Utah. German Research Foundation (DFG) Grant PF 950/1-1 (to MP). These funding organizations provided unrestricted grants and had no role in the design or conduct of this research. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. The funding organization had no role in study design, data collection, analysis, or interpretation, or the writing of the report. Publisher Copyright: © 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85117422474&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41598-021-99977-4
DO - https://doi.org/10.1038/s41598-021-99977-4
M3 - Article
C2 - 34650220
SN - 2045-2322
VL - 11
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 20446
ER -