SCH: Artificial Intelligence enabled multi-modal sensor platform for at-home health monitoring of patients

SCH：人工智能支持的多模式传感器平台，用于患者的家庭健康监测

• 批准号: 10816667
• 负责人: Imon Banerjee
• 金额: $ 30万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816667
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Adult; Algorithms; Arizona; Artificial Intelligence; Biological Assay; Biological Markers; Cardiovascular system; Caring; Cellular Phone; Clinic; Clinical; Clinical Trials; Clinics and Hospitals; Communication; Computer Vision Systems; Computerized Medical Record; Computers; Creatinine; Data; Data Set; Detection; Development; Devices; Diagnosis; Disease; Early Diagnosis; Electrical Engineering; Electrocardiogram; Energy consumption; Engineering; Evaluation; Event; Fatigue; Future; Gender; Goals; Guidelines; Health; Health Status; Healthcare; Home; Hospitals; Image; Ink; Institution; Intelligence; Lead; Learning; Medical; Medical Records; Microfluidics; Mission; Modeling; Monitor; National Institute of Biomedical Imaging and Bioengineering; Observational Study; Participant; Patient Monitoring; Patient-Focused Outcomes; Patients; Prevention; Printing; Process; Protocols documentation; Public Health Informatics; Reader; Records; Recurrence; Research; Risk; Risk Assessment; Running; Sampling; Scientist; Signal Transduction; Skin; Strategic vision; Survivors; System; Technical Expertise; Technology; Training; Travel; Urea; Urine; Urologist; Visit; Work; acute care; age stratification; analog; artificial intelligence algorithm; care burden; cohort; comorbidity; deep learning; demographics; design; detection assay; detection platform; encryption; flexibility; follow-up; health management; high risk; hospital readmission; improved; innovation; insight; mobile application; model design; mortality; multimodality; next generation; non-invasive system; patient health information; personalized predictions; point of care; poor health outcome; readmission rates; risk prediction; sensor; sensor technology; skin patch; smartphone application; transmission process; urinary; volunteer; wearable device; wireless

Acute kidney injury (AKI) is a commonly encountered medical problem that is associated with poor health outcomes in survivors, including increased mortality and re-admission to the hospital. Despite their high-risk status, only a small fraction (<10%) of patients receive specialized nephrologist follow-up after AKI episode. The low rate of follow-up care is due to lack of clear guidelines as well as reluctance on part of patients due to several reasons such as hospital fatigue, long travel times and unwillingness to add more doctors to the care team. To address the gap in care for AKI survivors, we propose an artificial intelligence (AI) enabled, MUlti-modal SEnsor (MUSE) platform for at-home use that can monitor patient health automatically, perform risk assessment for AKI recurrence, and alert the patient to seek specialized care. MUSE comprises of – 1) a colorimetric dipstick for capturing concentration of bio-markers (creatinine, urea, pH and lactate) in urine; 2) a near-field communication (NFC) powered stretchable, battery-less, single-lead electrocardiogram (ECG) skin patch that records ECG since cardiovascular complications is a strong predictor for AKI recurrence; 3) an AI-enabled mobile application that acquires sensor data (from urine sample and ECG) and runs an on-device deep learning fusion AI model to combine sensor data and patient medical record (past co-morbidities and demographics) for precision and personalized predictions. We will harness capabilities of smartphone for several key tasks - a) capture images of the dipstick sensor with built-in camera; b) act as NFC reader for ECG patch; c) run the computer vision and AI algorithms natively on-board without requiring network connection, and encrypt patient data locally. The AI model will be trained and validated on a large retrospective dataset collected from patients at Mayo Clinic Hospital, and the sensor system functionality will be validated with an observational study on 20 adult participants (10 healthy and 10 AKI patients) at Mayo. The proposed research has the potential to drive innovations in producing the next generation of intelligent wearables that performs fusion of multi-modal sensor data and EMR for early detection of underlying health issues with high accuracy. A successful realization of the proposal aims will pave the way for a future, large-scale clinical trial with our sensor platform.

急性肾脏损伤（AKI）是一个通常遇到的医疗问题，与健康状况不佳有关 生存的结果，包括增加死亡率和再入院。尽管他们高风险 状态，只有一小部分（<10％）的患者在AKI发作后接受专门的肾脏科医生随访。 较低的随访率是由于缺乏明确的准则以及不愿对应得的患者的一部分不愿 有几个原因，例如医院疲劳，长途旅行时间和不愿意向更多医生添加更多医生 护理团队。为了解决对AKI幸存的差距，我们建议启用人工智能（AI）， 可以自动监控患者健康的家庭使用的多模式传感器（MUSE）平台，执行 对AKI复发的风险评估，并提醒患者寻求专业护理。缪斯包括 - 1） 用于捕获尿液中生物标记浓度（肌酐，尿素，pH和裂缝）浓度的比色尺。 2）近场通信（NFC）可伸展的无电池，单铅心电图（ECG） 记录ECG的皮肤斑块，因为心血管并发症是AKI复发的有力预测因子。 3） 一个启用AI的移动应用程序，该应用程序可获取传感器数据（从尿液样本和ECG），并运行一个在设备深度学习融合AI模型中，以结合传感器数据和患者病历（过去的合并症 人口统计学）用于精确和个性化的预测。我们将利用智能手机的功能 对于几个关键任务 - a）用内置相机捕获深度传感器的图像； b）充当NFC阅读器 用于ECG补丁； c）在不需要网络的情况下，运行计算机视觉和AI算法本地板上 连接并在本地加密患者数据。 AI模型将在大型上进行培训和验证 从梅奥诊所医院收集的患者收集的回顾性数据集，传感器系统功能将 通过对梅奥（Mayo）的20名成年参与者（10名健康和10名AKI患者）进行的检查验证。 拟议的研究有可能推动创新，以生产下一代智能 执行多模式传感器数据和EMR融合的可穿戴设备，以及早检测基础健康 高精度的问题。该提案的成功实现目标将为我们的传感器平台提供未来的大规模临床试验铺平道路。