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

SCH：具有对抗性知识蒸馏的多维微流控唾液传感器，用于牙周炎和合并症的护理点评估

• 批准号: 10493410
• 负责人: Mathew Thoppil Mathew
• 金额: $ 29.58万
• 依托单位: ILLINOIS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10493410
• 关键词: Address; Adult; Area; Artificial Intelligence; Awareness; Bioinformatics; Biological Markers; Biology; Biosensor; Caring; Categories; Centers for Disease Control and Prevention (U.S.); Clinical; Collection; Communication; Computational Biology; Data; Data Analyses; Data Reporting; Data Science; Dental; Dentists; Detection; Devices; Diagnosis; Diagnostic; Disease; Early Diagnosis; Elements; Future; Goals; Health; Healthcare; Home; Individual; Inflammation; Intervention; Knowledge; Lab-On-A-Chips; Label; Learning; Machine Learning; Measurement; Measures; Mediator of activation protein; Medical; Microfluidic Microchips; Microfluidics; Modality; Monitor; Mouth Diseases; National Institute of Dental and Craniofacial Research; Oral; Oral health; Output; Pain; Patients; Periodontal Diseases; Periodontitis; Prediabetes syndrome; Privacy; Research; Saliva; Salivary; Sampling; Secure; Signal Transduction; Students; System; Systemic disease; Tooth structure; Training; aged; base; bone; care systems; clinical decision support; clinical examination; comorbidity; computerized data processing; cost; craniofacial; deep learning; design; diagnostic value; disease diagnosis; disorder control; disorder risk; diverse data; empowered; human subject; improved; indexing; individual patient; inflammatory bone loss; innovation; liquid biopsy; machine learning framework; machine learning method; machine learning model; microbial; miniaturize; multimodality; nanoparticle; nanopore; novel; oral care; personalized medicine; point of care; predictive modeling; procedure cost; programs; prototype; public health relevance; sensor; signal processing; soft tissue; solid state; teacher; tool; user-friendly

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.

本研究的目标是开发一种传感器装置原型，以快速测量各种阵列