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TRUVACE RECORD VERSION
record: TRV-2026-1627
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-16T22:02:56.206953Z
status: published
lens: g_space
sector: health
headline: Interpretable Machine Learning for Course Prediction in Central Serous Chorioretinopathy Using Baseline OCT from a Multicenter Cohort
dek: 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…
gain_title: Automatically detected OCT biomarkers at first presentation predicted whether treatment-naïve CSC eyes would become chronic, which may support prognosis and patient counseling.
problem_title: (none)
trace_subject: (none)
gain_reading: Automatically detected OCT biomarkers at first presentation predicted whether treatment-naïve CSC eyes would become chronic, which may support prognosis and patient counseling.
gain_evidence: Automatically detected OCT biomarkers can predict CSC chronicity at initial presentation with good discriminatory performance. | may improve prognostication, facilitate patient counseling, and help identify individuals at increased risk of persistent CSC
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built an interpretable Random Forest model using retinal measurements pulled automatically from the first OCT scan of 329 untreated eyes with central serous chorioretinopathy. Eyes were later labeled acute or chronic depending on whether subretinal fluid resolved during follow-up averaging 4.2 years.

The findings suggest that early scans already carry signals about who is likely to develop persistent disease, which could shape counseling and risk identification. The work is retrospective, so it shows predictive association in past records rather than proof that using the tool improves patient outcomes.
limitation: 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.
tag: Evidence-backed gain
key_points: The cohort covered 329 treatment-naïve eyes from 291 patients across eight international centers. | Of the eyes, 92 (28.0%) were acute and 237 (72.0%) were chronic CSC. | Chronic CSC patients were significantly older, at 51.7 ± 12.6 versus 43.2 ± 9.6 years. | Features included outer nuclear layer and PR+RPE thickness, SRF and PED volumes, and biomarker probabilities across ETDRS regions. | SHAP values were used to rank biomarker importance in the classifier.
rundown: The study used an AI-based platform, the Discovery OCT Biomarker Detector from RetinAI AG, to quantify retinal layer thicknesses and fluid volumes in central, parafoveal, and perifoveal regions from baseline scans.

Chronicity was defined by whether subretinal fluid resolved on follow-up. Model discrimination was assessed with area under the receiver operating characteristic curve, and SHAP values explained which biomarkers drove predictions.

The authors conclude that markers of outer retinal integrity, fluid configuration, and RPE dysfunction may improve prognostication and help identify individuals at increased risk of persistent CSC.
sources:
- peer_reviewed | American Journal of Ophthalmology | https://doi.org/10.1016/j.ajo.2026.10.002 | 2026-10-08
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