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: 10685431
• 负责人: Mathew Thoppil Mathew
• 金额: $ 29.82万
• 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/10685431
• 关键词: Address; Adult; Area; Artificial Intelligence; Awareness; Bioinformatics; Biological Markers; Biology; Biosensor; Caring; Categories; Clinical; Collection; Communication; Computational Biology; Data; Data Analyses; Data Reporting; Data Science; Dental; Dentists; Detection; Devices; Diagnosis; Diagnostic; Dimensions; Disease; Early Diagnosis; Elements; Future; Goals; Health; Healthcare; Home; Individual; Inflammation; Inflammatory; Intervention; Knowledge; Lab On A Chip; Label; Learning; Machine Learning; Measurement; Measures; Mediator; 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; Process; Research; Saliva; Salivary; Sampling; Secure; Signal Transduction; Students; System; Systemic disease; Tooth structure; Training; aged; bone; bone loss; 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; empowerment; human subject; improved; indexing; individual patient; innovation; liquid biopsy; machine learning framework; machine learning method; machine learning model; microbial; miniaturize; multimodality; nano; nanoparticle; nanopore; novel; oral care; personalized medicine; point of care; predictive modeling; procedure cost; programs; prototype; public health relevance; risk prediction; sensor; signal processing; soft tissue; solid state; teacher; tool; transmission process; user protection; 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.

这项研究的目标是开发一种传感器设备原型来快速测量一系列不同的 唾液生物标志物作为新型机器学习（ML）方法的输入，可以预测牙周炎和 我们的长期目标是开发一种快速、用户友好且低成本的即时护理 (POC) 设备，可在牙医办公室或家庭中使用，可快速集成和交叉数据。 支持患者管理。它涉及数据科学、计算生物学、 NIDCR 的生物信息学项目整合和分析大量和多样化的数据，以更好地 了解牙科、口腔和颅面生物学和疾病。 根据疾病预防控制中心的数据，近 50% 的成年人患有某种形式的牙周炎。 进展性的，患者通常只有在晚期才寻求专业护理，此时晚期、疼痛 需要昂贵的手术来控制疾病或更换丢失的牙齿。 需要对个体患者水平的疾病进行研究，并且人们越来越意识到多种生物标志物是 我们率先建立了预测模型。 基于唾液中大量牙周炎相关生物标志物的测量； 集成多传感器模式和机器学习 (ML) 模型将推进临床目标 因此，我们将开发和应用牙周炎的早期诊断，以实现早期临床干预。 三种独特的传感器模式，用于检测与各个阶段相关的唾液分析物的浓度 牙周进展，即炎症、软组织破坏或骨质破坏（目标 1）。 传感器输出和临床检查都将用于通过新型多模态训练机器学习模型 对抗性知识蒸馏 ML 框架，通过部分内容促进准确的早期预测 将嵌入纵向数据表示（目标 2）。 在单个微流体装置中，将采样、检测和数据分析等步骤结合在一起 集成的片上实验室，并允许预处理的传感器数据仅传输可操作的 向外部平台提供信息以保护用户的隐私（目标 3）。 进行不引人注目、准确且频繁的基于唾液的自我监测，并提供详细的医疗数据 它将成为未来个性化医疗的有效工具，并显着支持临床决策。 改善患者的口腔健康。