ArticleBiomedical optics express2025
Convolutional neural network-based differentiation of intraocular inflammatory cells with ultrahigh-resolution OCT.
Article in Biomedical optics express, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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9 authors.
Funding
Abstract
The differential diagnosis of uveitis is broad and often challenging. A key indicator of intraocular inflammation is the presence of cells in the aqueous or vitreous humor. We developed a lightweight convolutional neural network (CNN) model capable of classifying intraocular inflammatory cell types using ultrahigh-resolution optical coherence tomography (OCT). The model was trained and optimized using OCT images of known cell types-mononuclear cells and granulocytes-and achieved an accuracy of 88.4 ± 0.6%, with an area under the receiver operating characteristic (ROC) curve of 94.3 ± 0.4%. The inflammatory cell compositions predicted by the model were consistent with clinical diagnoses in uveitis patients. This approach offers a promising tool for the diagnosis and monitoring of intraocular inflammation in uveitis.
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