The goal of this research is to develop a sensor device prototype to rapidly measure an array of diverse salivary biomarkers as input for novel machine learning (ML) methods that can predict periodontitis and monitor periodontal progression. Our long-term goal is to develop a rapid, user-friendly, and low-cost pointof-care (POC) device, for use in either a dentist’s office or at home, that rapidly integrates and analyzes data to support patient management. It addresses the priority area of the Data Science, Computational Biology, and Bioinformatics Program of NIDCR in integrating and analyzing high-volume and diverse data to better understand dental, oral, and craniofacial biology and diseases. According to the CDC, nearly 50% adults have some form of periodontal disease. Perioodontitis is silently progressive and patients often seek professional care only in an advanced stage where advanced, painful and costly procedures are needed to control disease or replace lost teeth. Early detection of periodontal disease at an individual patient level is required and there is growing awareness that multiple biomarkers are valued in predicting risk of disease in individuals. We hypothesize that predictive models can be established based on the measurements of a large set of periodontitis-associated biomarkers in saliva; a sensor device that integrates multi-sensor modalities and the machine learning (ML) models will advance the clinical goal of early diagnosis of periodontitis to enable earlier clinical interventions. Thus, we will develop and apply three distinctive sensor modalities for detecting concentrations of salivary analytes relevant to various stages of periodontal progression, i.e., inflammation, soft tissue destruction or bone destruction (Aim 1). Data from both sensor outputs and clinical examination will be used to train ML models via a novel multi-modal adversarial knowledge distillation ML framework, which promotes accurate early prediction with partial longitudinal data representations (Aim 2). The multi-sensor modalities and the ML models will be embedded in a single microfluidic device, incorporating steps such as sampling, detection, and data analysis as an integrated lab-on-a-chip, and permitting the sensor data preprocessed to transmit only the actionable information to the outside platform to protect the user's privacy (Aim 3). Such a device is anticipated to offer for unobtrusive, accurate, and frequent saliva-based self-monitoring, and provide detailed medical data to support clinical decisions. It will be an effective tool for future personalized medicine and dramatically improve patients' oral health.