# SCH: Multidimensional Microfluidic Salivary Sensor with Adversarial Knowledge Distillation for Point-of-Care Assessment of Periodontitis and Comorbidities

> **NIH NIH R01** · ILLINOIS INSTITUTE OF TECHNOLOGY · 2024 · $84,708

## Abstract

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.

## Key facts

- **NIH application ID:** 11170093
- **Project number:** 3R01DE031832-04S1
- **Recipient organization:** ILLINOIS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Mathew Thoppil Mathew
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $84,708
- **Award type:** 3
- **Project period:** 2021-09-23 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11170093

## Citation

> US National Institutes of Health, RePORTER application 11170093, SCH: Multidimensional Microfluidic Salivary Sensor with Adversarial Knowledge Distillation for Point-of-Care Assessment of Periodontitis and Comorbidities (3R01DE031832-04S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11170093. Licensed CC0.

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