Open Access CRIS of the University of Bern
Interpretable Machine Learning for Course Prediction in Central Serous Chorioretinopathy Using Baseline OCT from a Multicenter Cohort
By Lorenzo Ferro Desideri, Nina Eldridge, Lieselotte Berger and 20 others.
Abstract
Purpose
To predict disease clinical course in central serous chorioretinopathy (CSC) using an interpretable machine learning model applied to baseline artificial intelligence (AI)-derived optical coherence tomography (OCT) biomarkers acquired at first presentation.
Design
Multicenter, retrospective cohort study.
Participants
Overall, 329 treatment-naïve CSC eyes recruited across eight international centers in Europe, the Middle East, Asia, and North Africa.
Methods
Treatment-naïve CSC patients were retrospectively included and classified, based on follow-up, as acute or chronic CSC according to subretinal fluid (SRF) resolution. Baseline OCT biomarkers were extracted using an AI-based platform (Discovery OCT Biomarker Detector; RetinAI AG, Switzerland), which quantified retinal layer thicknesses, including the outer nuclear layer (ONL) and photoreceptor-retinal pigment epithelium (PR+RPE) complex, fluid volumes (SRF and RPE detachment [PED]), and biomarker probabilities, across central, parafoveal, and perifoveal ETDRS regions. These features used to develop an interpretable machine learning model predicting future CSC chronicity.