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.