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.

这项研究的目的是开发传感器设备原型，以快速测量潜水员 唾液生物标志物作为新机器学习的输入（ML）方法，可以预测牙周炎和 监测牙周进展。我们的长期目标是开发一种快速，用户友好和低成本的护理（POC）设备，用于牙医办公室或在家中，该设备可以迅速整合和分析数据 支持患者管理。它解决了数据科学的优先领域，计算生物学， NIDCR的生物信息学计划在整合和分析大量和不同数据以更好 了解牙科，口服和颅面生物学和疾病。 根据CDC的数据，将近50％的成年人患有某种形式的牙周疾病。骨膜炎静静 进步和患者经常只在高级，痛苦的高级阶段寻求专业护理 需要昂贵的程序来控制疾病或替代牙齿丢失。牙周发现的早期检测 需要单个患者水平的疾病，并且越来越认识到多种生物标志物是 在预测个体疾病风险方面有价值。我们假设可以建立预测模型 基于唾液中大量牙周炎相关的生物标志物的测量；传感器设备 整合多传感器方式和机器学习（ML）模型将推进临床目标 牙周炎的早期诊断，以实现早期临床干预措施。那我们将开发和应用 三种独特的传感器方式，用于检测与各个阶段有关的唾液分析浓度 牙周进展，即炎症，软组织破坏或骨骼破坏（AIM 1）。来自 传感器输出和临床检查都将用于通过新型多模式训练ML模型 对抗知识蒸馏ML框架，该框架可促进准确的早期预测 纵向数据表示（AIM 2）。多传感器模式和ML模型将嵌入 在单个微流体设备中，编码采样，检测和数据分析等步骤 集成的实验室芯片，并允许使用传感器数据进行预处理以仅传输可起诉的 信息到外部平台保护用户的隐私（AIM 3）。预计这样的设备将提供 对于不引人注目，准确且经常基于唾液的自我监控，并提供详细的医疗数据 支持临床决策。它将是未来个性化医学的有效工具，并且很大程度上 改善患者的口腔健康。