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（阿基）是一种常见的医疗问题，与健康状况不佳有关 幸存者的结局，包括死亡率增加和再次入院。尽管他们的高风险 在AKI患者中，只有一小部分（<10%）的患者在阿基发作后接受了专门的肾病学家随访。 随访率低的原因是缺乏明确的指导方针，以及部分患者不愿接受随访， 几个原因，如医院疲劳，长途旅行时间和不愿意增加更多的医生， 护理团队为了解决阿基幸存者护理方面的差距，我们提出了一种人工智能（AI）， 多模态传感器（MUSE）平台，可在家中使用，可自动监测患者健康状况， 阿基复发的风险评估，并提醒患者寻求专业护理。MUSE包括：1） 用于捕获尿中生物标志物（肌酸酐、尿素、pH和乳酸盐）浓度的比色试纸; 2)近场通信（NFC）供电的可伸展、无电池、单导联心电图（ECG） 记录ECG的皮肤贴片，因为心血管并发症是阿基复发的强预测因子; 3） 支持AI的移动的应用程序，可获取传感器数据（来自尿样和ECG），并运行设备上的深度学习融合AI模型，以将联合收割机传感器数据和患者病历（过去的合并症）结合起来 和人口统计数据）进行精确和个性化的预测。我们将利用智能手机的功能 用于几项关键任务- a）使用内置摄像头捕获油尺传感器的图像; B）充当NFC读取器 对于ECG贴片; c）在不需要网络的情况下在本机上运行计算机视觉和AI算法 连接，并在本地加密患者数据。人工智能模型将在大型 从马约诊所医院的患者中收集的回顾性数据集，传感器系统功能将 通过在马约对20名成人参与者（10名健康和10名阿基患者）进行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制造的创新。 可穿戴设备，执行多模态传感器数据和EMR的融合，以早期检测潜在的健康状况 高精度的问题。该提案目标的成功实现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。