Monitoring of disease-induced skin VOC patterns from handheld and wearable chemical sensors

通过手持式和可穿戴化学传感器监测疾病引起的皮肤 VOC 模式

基本信息

批准号：
10651755
负责人：
CRISTINA ELIZABETH DAVIS
金额：
$ 100.22万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-22 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10651755
关键词：
Adult Aerospace Engineering Artificial Intelligence Asthma Attention deficit hyperactivity disorder Automatic Data Processing Benchmarking Biomedical Technology Blood Pressure California Catalogs Chemicals Childhood Chronic Disease Chronic Obstructive Pulmonary Disease Clinical Clinical Management Connective Tissue Diseases Coupled Data Data Analyses Degenerative polyarthritis Detection Development Devices Diagnosis Diagnostic Diagnostic tests Differential Diagnosis Disease Disease Management Eczema Engineering Enterovirus Infections Environment Exhalation Fever Fingerprint Flare Fragile X Premutation Funding Galvanic Skin Response Gases Goals Health Healthcare Heart Rate Home Humidity Hypersensitivity skin testing Individual Joints Knowledge Lead Link Lung Lung diseases Machine Learning Mass Spectrum Analysis Measurement Measures Mechanics Medical Mental Health Methods Monitor National Institute of Biomedical Imaging and Bioengineering National Institute of Environmental Health Sciences Output Oxygen Patients Pattern Pediatric Hospitals Phase Philadelphia Psoriasis Psoriatic Arthritis Publishing Pulmonary Embolism Pulmonary Fibrosis Pulmonary Hypertension Pulse Rates RADx Rapid diagnostics Reagent Regulatory Pathway Respiration Respiratory Signs and Symptoms Respiratory Syncytial Virus Infections Rheumatoid Arthritis Sampling Sarcoidosis Schizophrenia Sickle Cell Anemia Site Skin Skin Temperature Spectrometry Standardization Symptoms System Systemic disease Testing Time Training United States National Institutes of Health Urinary tract infection Urine Virus Diseases Vulnerable Populations Weight Work artificial intelligence algorithm asthmatic patient autism spectrum disorder biomarker discovery clinical research site cohort commercialization cost data integration data streams detector diagnostic tool disease diagnosis graphical user interface health care settings improved influenza infection instrument mHealth machine learning algorithm manufacture metabolomics minimally invasive novel novel diagnostics pediatric patients point-of-care diagnostics portability power consumption programs real time monitoring research clinical testing sensor skin disorder telehealth tool volatile organic compound wearable device wearable sensor technology

项目摘要

Project Summary/Abstract: This project will bring two skin VOC sensors (hand-held, wearable) into clinical use to improve rapid diagnostics for a range of health conditions. Skin VOC monitoring is a new concept with potential to transform healthcare. Our hypothesis is that miniature skin VOC analysis devices can be coupled with vital sign sensors to measure disease signatures in real-time faster than a traditional differential diagnosis. The proposal has four goals: (1) adapt our current volatile organic compound (VOC) detector into a hand-held format for gas phase skin-emitted metabolites, coupled to non-invasive vital sign sensors and artificial intelligence machine learning (AI/ML) algorithms; (2) deploy our hand-held skin VOC system on 20 diseases over 5 years; (3) adapt our current wearable vital monitoring system to include our skin VOC detector, and use this to monitor persistent asthma patients for disease flares; (4) prepare for our project and devices to move through commercial manufacturing, standardization and FDA regulatory approval. To meet these goals, we plan the following: in Aim #1, we adapt our miniature VOC detection device for skin measurements, and couple it with 7 commercial-off-the-shelf vital sign sensors (skin temperature, pulse rate, respiration rate, heart rate, oxygen saturation, galvanic skin response, skin humidity). Our miniature differential mobility spectrometry detector is coupled with a chip-based preconcentrator and miniature gas chromatograph column for chemical separation and detection. Individual components have already been developed. Under direction of MPI Prof. Davis, UC Davis Chair of Mechanical and Aerospace Engineering, a team of engineers will adapt these pieces together into a hand-held unit for skin VOC sampling/analysis. Co-I Prof. Chuah will guide development of AI/ML capability for automated data processing and interpretation from the integrated VOC and vital sign data streams. In Aim #2, we will use this hand-held system at two different clinical sites to develop AI/ML signatures for 20 different diseases compared to appropriately selected controls. The UC Davis site led by MPI Nicholas Kenyon will focus on: 2 skin diseases (eczema, psoriasis), 7 lung diseases (asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, pulmonary hypertension, pulmonary embolism, sarcoidosis, sickle cell disease with respiratory symptoms), 3 joint and connective tissue diseases (rheumatoid arthritis, psoriatic arthritis, osteoarthritis), 4 mental health diseases (attention deficit hyperactivity disorder, autism, schizophrenia, Fragile X premutation with mental health symptoms). The Children’s Hospital of Philadelphia site lead by Co-I Audrey John will focus on: 4 pediatric fevers (urinary tract infection, enterovirus infection, respiratory syncytial virus infection, influenza infection). In Aim #3, our team will combine our current wearable vital sign sensors with our miniature VOC sensor, and identifying a novel profile for persistent asthma disease flares from both data streams. Aim #4 will develop a manufacturing, commercialization, standardization and FDA regulatory pathway for our devices/tests. These efforts are in conjunction UC Davis start-up company SensIT Ventures.

项目摘要/摘要：该项目将把两种皮肤VOC传感器（手持式、可穿戴式）引入临床用于改善一系列健康状况的快速诊断是一个新概念。我们的假设是微型皮肤 VOC 分析设备可以耦合起来。使用生命体征传感器比传统鉴别诊断更快地实时测量疾病特征。该提案有四个目标：(1) 将我们当前的挥发性有机化合物 (VOC) 检测器改造为手持式检测器气相皮肤发射代谢物的格式，与非侵入性生命体征传感器和人工智能机器学习（AI/ML）算法；（2）将我们的手持式皮肤VOC系统部署在20种疾病上超过 5 年；(3) 调整我们当前的可穿戴生命监测系统以包括我们的皮肤 VOC 检测器，并使用这是为了监测持续性哮喘患者的疾病发作；(4) 为我们的项目和设备的移动做好准备；为了实现这些目标，我们通过商业制造、标准化和 FDA 监管批准。计划如下：在目标#1中，我们采用微型 VOC 检测设备进行皮肤测量，并结合它配有 7 个商用现成的生命体征传感器（皮肤温度、脉搏率、呼吸率、心率、氧饱和度、皮肤电反应、皮肤湿度）。检测器与基于芯片的预浓缩器和微型气相色谱柱相结合，用于化学分析分离和检测的各个组件已经在 MPI 教授的指导下开发出来。 Davis，加州大学戴维斯分校机械和航空航天工程系主任，工程师团队将改编这些作品组合成一个用于皮肤 VOC 采样/分析的手持式装置，Chuah 教授将指导开发。 AI/ML 功能可对集成 VOC 和生命体征数据进行自动数据处理和解释在目标 2 中，我们将在两个不同的临床站点使用该手持式系统来开发 AI/ML 签名。由 MPI Nicholas 领导的加州大学戴维斯分校网站对 20 种不同的疾病进行了比较。凯尼恩将重点关注：2种皮肤病（湿疹、牛皮癣）、7种肺部疾病（哮喘、慢性阻塞性肺病）肺部疾病、肺纤维化、肺动脉高压、肺栓塞、结节病、镰状细胞病具有呼吸道症状的疾病）、3种关节和结缔组织疾病（类风湿性关节炎、银屑病） 4种心理健康疾病（注意力缺陷多动障碍、自闭症、精神分裂症、具有心理健康症状的脆性 X 前突变）。由 Co-I 领导的费城儿童医院网站。奥黛丽·约翰将重点关注：4种小儿发烧（尿路感染、肠道病毒感染、呼吸道合胞病毒感染）在目标#3中，我们的团队将结合我们当前的可穿戴生命体征传感器。使用我们的微型 VOC 传感器，并识别来自两种疾病的持续性哮喘疾病发作的新特征目标 #4 将开发制造、商业化、标准化和 FDA 监管。我们的设备/测试的途径是与加州大学戴维斯分校的初创公司 SensIT Ventures 合作进行的。