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，以快速测量各种不同的阵列 唾液生物标志物作为新的机器学习(ML)方法的输入，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POC设备，无论是在牙医办公室还是在家里使用，都能快速集成和分析数据 以支持患者管理。它涉及数据科学、计算生物学、 和NIDCR的生物信息学程序整合和分析海量和多样化的数据，以更好地 了解牙科、口腔和颅面生物学及疾病。 根据疾控中心的数据，近50%的成年人患有某种形式的牙周病。牙周炎默默地 进展性和患者通常只在晚期寻求专业护理，在晚期，疼痛 而且需要昂贵的手术来控制疾病或替换掉牙。牙周早期检测 患者个体水平的疾病是必需的，而且人们越来越意识到多个生物标志物是 在预测个人的疾病风险方面有价值。我们假设可以建立预测模型 基于对唾液中大量牙周炎相关生物标志物的测量；一种传感器设备 将多传感器模式和机器学习(ML)模型相结合将推进临床目标 对牙周炎进行早期诊断，以便进行更早的临床干预。因此，我们将开发和应用 三种独特的传感器模式用于检测与不同阶段相关的唾液分析物浓度 牙周进展，即炎症、软组织破坏或骨破坏(目标1)。数据来自 传感器输出和临床检查将通过一种新的多模式训练ML模型 对抗性知识蒸馏ML框架，通过局部预测促进准确的早期预测 纵向数据表示法(目标2)。多传感器模式和ML模型将被嵌入 在单个微流控设备中，将采样、检测和数据分析等步骤整合为 集成了片上实验室，并允许经过预处理的传感器数据仅传输可操作的 将信息提供给外部平台以保护用户隐私(目标3)。这样的设备预计将提供 用于不显眼、准确和频繁的唾液自我监测，并提供详细的医疗数据 支持临床决策。它将成为未来个性化医疗的有效工具，并显著 改善患者的口腔健康。