Journal
NATURE BIOMEDICAL ENGINEERING
Volume 4, Issue 6, Pages 624-635Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41551-020-0534-9
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Funding
- Stanford Institutes of Medicine Summer Research Program
- Canary Foundation
- NIH/NCI Training Grant [T32 CA118681]
- National Center for Research Resources
- National Center for Advancing Translational Sciences, National Institutes of Health [UL1 TR001085]
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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. A 'smart' toilet that uses pressure and motion sensors, biometric identification, urinalysis strips, a computer-vision uroflowmeter and machine learning longitudinally tracks biomarkers of health and disease in the user's urine and stool.
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