AI-enabled multiplexed vertical flow diagnostic platform integrating rapid biomarker assays, optical image acquisition, and machine learning analytics to transform multidimensional biochemical signals into clinically actionable health classifications for early disease detection and personalized risk assessment.
Gestational diabetes mellitus (GDM) is a multifactorial metabolic condition that significantly impacts both maternal and fetal health, often developing without clear early symptoms. Delayed diagnosis can lead to serious complications, including preeclampsia, fetal overgrowth, and increased lifetime risk of metabolic disease. Current screening approaches rely on single-analyte testing or oral glucose tolerance procedures that are time-intensive, uncomfortable for patients, and insufficient to capture the complex biological pathways underlying disease progression.
To address this unmet clinical need, the Optical Bio-Sensing Laboratory is developing a next-generation multiplexed diagnostic platform that combines molecular sensing, nanotechnology, and artificial intelligence to enable earlier and more precise detection of gestational diabetes. The system utilizes aptamer-based recognition of multiple GDM-associated biomarkers, integrated into a vertical flow assay (VFA) architecture, enabling simultaneous measurement of biochemical signals that reflect the multidimensional nature of disease physiology.
Advanced deep learning analytics are applied to interpret complex optical and electrochemical signals, enabling the identification of subtle biomarker interactions and predictive disease signatures that are not detectable by conventional screening methods. This approach supports earlier diagnosis, improved maternal–fetal risk stratification, and increased diagnostic confidence through enhanced sensitivity and specificity.
Designed for scalable deployment in both centralized laboratories and decentralized point-of-care environments, the platform requires minimal sample volume and supports rapid, user-friendly operation. By integrating multiplexed molecular detection with AI-driven interpretation, this technology represents a transformative step toward personalized prenatal diagnostics, enabling proactive clinical decision-making and improved pregnancy outcomes across diverse healthcare settings.