AI-assisted Raman spectroscopy system for biomarker analysis of aqueous humor

Abstract

A non-invasive diagnostic system is disclosed that integrates Raman spectroscopy with Artificial Intelligence (AI) to detect, classify, and quantify biomarkers and pharmacologic agents in the aqueous humor of the anterior chamber of the eye. The system includes a handheld or slit lamp-mounted Raman probe, a spectral acquisition and preprocessing pipeline, and an AI analysis engine trained to recognize disease-associated molecular signatures. The platform supports real-time diagnosis of glaucoma, uveitis, and other anterior segment conditions, enabling early detection, risk stratification, and longitudinal monitoring. The AI component is capable of incorporating newly discovered biomarkers and contextual factors such as medication use, diurnal variation, and anatomical sampling location. Diagnostic outputs are presented with confidence scores and may include disease classification, biomarker trends, or pharmacologic exposure levels. Built-in safety protocols ensure ANSI Z136.1-compliant laser use, while explainable AI tools and federated learning enhance transparency and clinical adaptability.

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