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.

急性肾损伤 (AKI) 是一种常见的医疗问题，与健康状况不佳有关 幸存者的结果，包括死亡率增加和再次入院，尽管他们的风险很高。 状态，只有一小部分（<10%）患者在 AKI 发作后接受专门的肾病专家随访。 随访率低的原因是缺乏明确的指导方针以及部分患者因自身原因不愿意接受随访。 由于医院疲劳、出行时间长以及不愿增加更多医生等多种原因 为了解决 AKI 幸存者护理方面的差距，我们提出了一个启用人工智能 (AI) 的团队。 适合家庭使用的多模态 SEnsor (MUSE) 平台，可以自动监测患者健康状况、执行 AKI 复发的风险评估，并提醒患者寻求专门护理，包括 – 1) 用于捕获尿液中生物标志物（肌酐、尿素、pH 值和乳酸）浓度的比色试纸； 2) 近场通信 (NFC) 供电的可伸缩、无电池、单导联心电图 (ECG) 记录心电图的皮肤贴片，因为心血管并发症是 AKI 复发的有力预测因素 3) 一款支持人工智能的移动应用程序，可获取传感器数据（来自尿液样本和心电图）并运行设备上的深度学习融合人工智能模型，以将传感器数据和患者医疗记录（过去的合并症）结合起来 和人口统计）进行精确和个性化的预测。我们将利用智能手机的功能。 用于多项关键任务 - a) 使用内置摄像头捕获量油尺传感器的图像 b) 充当 NFC 读取器 用于心电图补丁；c) 无需网络即可在机上本地运行计算机视觉和人工智能算法 连接，并在本地加密患者数据。人工智能模型将在大规模上进行训练和验证。 从梅奥诊所医院的患者收集的回顾性数据集，传感器系统功能将 通过 Mayo 的 20 名成年参与者（10 名健康患者和 10 名 AKI 患者）的观察性研究进行验证。 拟议的研究有可能推动下一代智能产品的创新 融合多模式传感器数据和 EMR 的可穿戴设备，以便及早检测潜在健康状况 成功实现该提案的目标将为未来使用我们的传感器平台进行大规模临床试验铺平道路。