Machine learning-empowered nanomaterial design and application for tear fluid-based point-of-care tests: from...

Tear fluid harbors a rich array of cellular and molecular biomarkers that mirror both ocular and systemic health, positioning it as an ideal medium for non-invasive point-of-care testing (POCT). Yet its analytical utility is constrained by sub-microliter sample volumes, dynamic compositional fluctuations, and trace-level biomarker concentrations. Nanomaterials, with their surface-to-volume ratio, programmable surface chemistry, and tunable optical and catalytic properties, have been engineered into miniaturized platforms to enable target enrichment, multiplexed recognition, and signal amplification within microliter samples. Building on this material foundation, machine learning (ML) is now reshaping the entire analytical workflow. Data-driven algorithms accelerate the in-silico discovery and rational optimization of functional nanomaterials. Deep learning models further decouple matrix interference to enable ultra-sensitive, multiplexed readouts, while downstream algorithms translate multi-biomarker profiles into actionable clinical decisions. This review summarizes design strategies for ML-empowered, tear fluid-based POCT and examines how nanomaterials address the inherent constraints of tear analysis. However, clinical translation remains limited by non-standardized tear collection and data acquisition, insufficient large-scale clinical validation, and concerns regarding algorithm robustness and interpretability. The review further outlines translational applications spanning disease diagnosis, patient stratification, therapeutic monitoring, and decentralized longitudinal surveillance to support biomarker-driven precision medicine.