Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA.
Nat Biomed Eng. 2020 Jun;4(6):624-635. doi: 10.1038/s41551-020-0534-9. Epub 2020 Apr 6.
Technologies for the longitudinal monitoring of a person's health are poorly integrated with clinical workflows, and have rarely produced actionable biometric data for healthcare providers. Here, we describe easily deployable hardware and software for the long-term analysis of a user's excreta through data collection and models of human health. The 'smart' toilet, which is self-contained and operates autonomously by leveraging pressure and motion sensors, analyses the user's urine using a standard-of-care colorimetric assay that traces red-green-blue values from images of urinalysis strips, calculates the flow rate and volume of urine using computer vision as a uroflowmeter, and classifies stool according to the Bristol stool form scale using deep learning, with performance that is comparable to the performance of trained medical personnel. Each user of the toilet is identified through their fingerprint and the distinctive features of their anoderm, and the data are securely stored and analysed in an encrypted cloud server. The toilet may find uses in the screening, diagnosis and longitudinal monitoring of specific patient populations.
用于长期监测个人健康的技术与临床工作流程整合不佳，很少为医疗保健提供者提供可采取行动的生物识别数据。在这里，我们描述了易于部署的硬件和软件，可通过数据收集和人体健康模型对用户的排泄物进行长期分析。“智能”马桶是一个独立的系统，利用压力和运动传感器自主运行，使用护理标准的比色分析方法分析用户的尿液，该方法从尿液分析条的图像中追踪红-绿-蓝值，使用计算机视觉作为尿流计计算尿液的流速和体积，并使用深度学习根据布里斯托粪便形态量表对粪便进行分类，其性能可与经过培训的医务人员相媲美。马桶通过用户的指纹和肛门的独特特征识别每个使用者，数据安全地存储在加密的云服务器中进行分析。这种马桶可能会在特定患者群体的筛查、诊断和长期监测中找到用途。
