aceInterpretable Machine Learning for Course Prediction in Central Serous Chorioretinopathy Using Baseline OCT from a Multicenter Cohort
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 retrospective…
Automatically detected OCT biomarkers at first presentation predicted whether treatment-naïve CSC eyes would become chronic, which may support prognosis and patient counseling.
The study was a retrospective cohort design, so it demonstrates predictive performance on past data rather than showing that using the model changes clinical decisions or outcomes.
Evidence
- Peer-reviewedAmerican Journal of Ophthalmology2026-10-08
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Truvace Impact Record TRV-2026-1627, v1: “Interpretable Machine Learning for Course Prediction in Central Serous Chorioretinopathy Using Baseline OCT from a Multicenter Cohort.” Truvace, 2026-10-16. /record/TRV-2026-1627 (accessed at citation time). sha256 58d524f3509f0c25…
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