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Health·G Space·Evidence-backed gain·Published 2026-08-15

PREDICTIVE OCT BIOMARKERS OF RETINAL CHANGES AND VISUAL OUTCOMES IN SILICONE OIL ENDOTAMPONADE IDENTIFIED BY ARTIFICIAL INTELLIGENCE

Abstract: Purpose To quantify retinal layer changes and visual outcomes in eyes with silicone oil (SO) endotamponade for rhegmatogenous retinal detachment using OCT biomarkers and prediction models. Methods Seventy-six eyes with SO endotamponade underwent macular volume OCT at SO insertion and just before SO removal. An automated segmentation tool quantified retinal nerve fiber layer (RNFL), ganglion cell layer + inner plexiform layer (GCL+IPL), other retinal layers, and fluid (SRF, IRF). Eyes with and without macular ede…

TRV-2026-0765Peer-reviewedPermanent record — cite & verify
PREDICTIVE OCT BIOMARKERS OF RETINAL CHANGES AND VISUAL OUTCOMES IN SILICONE OIL ENDOTAMPONADE IDENTIFIED BY ARTIFICIAL INTELLIGENCE

"CRE-003 OphaGo - Retinal Imaging Adapter - Huced Despro ITS" by Djoko Kuswanto is marked with CC0 1.0. To view the terms, visit https://creativecommons.org/publicdomain/zero/1.0/.

The quick read

In 76 eyes treated with silicone oil endotamponade for rhegmatogenous retinal detachment, researchers used an automated OCT segmentation tool and a random forest classifier to track retinal layer changes between oil insertion and removal and to predict categorical best-corrected visual acuity change.

The analysis found selective inner retinal thinning and identified those layer measures as the dominant predictors of visual outcome, while macular edema status and oil duration contributed minimally, suggesting AI-derived OCT biomarkers could help clinicians prognosticate recovery after oil removal, though generalizability beyond this single cohort remains untested in the supplied text.

Main points
  • 76 eyes with silicone oil endotamponade for rhegmatogenous retinal detachment had macular volume OCT at insertion and before removal.
  • Automated segmentation measured RNFL, GCL+IPL, other layers and fluid; random forest classifier explored predictors of categorical BCVA change.
  • RNFL thinned from 35.4 7 18.1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Gain

Automated AI segmentation of macular OCT quantified selective inner retinal thinning during silicone oil tamponade and identified RNFL, GCL+IPL and PR+RPE as strongest predictors of visual acuity change, enabling prognostication after oil removal.

The rundown

The study enrolled 76 eyes undergoing silicone oil endotamponade for rhegmatogenous retinal detachment, with OCT acquired at insertion and just before removal. An automated tool segmented RNFL, GCL+IPL, other layers and SRF/IRF fluid, and eyes were stratified by presence of macular edema during tamponade.

Results showed RNFL thinning from 35.4 7 18.1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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