ObjectiveTo develop and validate an artificial intelligence (AI)-driven pipeline to quantify vitreous hyperreflective foci (vHRF) from optical coherence tomography (OCT) images and assess their association with intraocular inflammation (IOI). DesignA retrospective analysis of a multi-center double-masked placebo-controlled clinical trial cohort. SubjectsA clinical analysis cohort of 369 patients from the GALLEGO clinical trial (Galegenimab vs placebo in patients with geographic atrophy (GA), clinical trial ID: NCT03972709). MethodsWe trained a deep learning segmentation model (UNETR) with a Vision Transformer backbone on 491 OCT B-scans with expert vHRF annotations from the GALLEGO, BURGUNDY (Neovascular Age-related Macular Degeneration; NCT04567303), and DOVETAIL (Uveitic Macular Edema; NCT06771271) clinical trials.We applied the optimized model to a clinical analysis cohort from the GALLEGO clinical trial, comprising 1,049 OCT volumes (34 images of IOI-positive cases), and evaluated the association between the resulting quantitative vHRF metrics and clinically diagnosed IOI. Main Outcome MeasuresSegmentation performance and the association of quantitative vHRF metrics with clinically diagnosed concurrent IOI, including ROC, precision-recall, predictive value, and eye-clustered regression analyses. ResultsThe UNETR model demonstrated strong segmentation performance.In the clinical cohort, IOIpositive eyes showed significantly elevated vHRF metrics (p<0.001), and vHRF volume density was the strongest biomarker for concurrent IOI detection (AUROC 0.84).In eye-clustered logistic GEE models, five filtered biomarkers were significantly associated with inflammation, with the strongest association for filtered vHRF density vHRF/µm 3 (OR per SD 1.62, 95% CI 1.26-2.07,p<0.001).At the optimal threshold for vHRF density, sensitivity was 67.7%, specificity 89.0%, and PPV was 16.9% despite an IOI prevalence of 3.2%. ConclusionsOur AI-driven pipeline accurately quantified vHRF from OCT images, and the resulting metrics, particularly vHRF volume density, were significantly associated with concurrent IOI.These findings support automated vHRF quantification as a promising imaging biomarker for J o u r n a l P r e -p r o o f inflammation assessment, although further validation in larger and more diverse datasets is needed.
Cohen et al. (Fri,) studied this question.