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.
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Restrictions apply to the availability of the medical training and validation data, which were used with permission of the participants for the current study, and are therefore not publicly available. Some of the data may be available from the authors on reasonable request, after permission from the Stanford University School of Medicine and/or the Seoul Song Do Hospital.
The codes may be available from the authors on reasonable request, after permission from the Stanford University School of Medicine and/or Seoul Song Do Hospital.
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We thank N. Baneai for insight and discussion regarding stool analysis; K. J. Chung for discussion on uroflowmetry implementation; P. Yock for discussion on overall project guidance and on managing intellectual property; C. Kim and I. Steinberg for helpful guidance on image reconstruction; C. T. Chan for the scientific proofreading of the manuscript; D. Chon (Albany High School, CA, USA) for her contribution to 3D CAD designs, and the real-time image analysis for the urinalysis module; the Stanford Clinical Laboratory for overviewing their urinalysis equipment; and staff at the Stanford Product Realization Laboratory, SPF, Stanford Byers Center for Biodesign, Stanford Urology Clinic and S. Taheri for their services. D.E. and S.-m.P. acknowledge the support of the Stanford Institutes of Medicine Summer Research Program. This work was supported in part by the Canary Foundation (to S.S.G.) and in part by NIH/NCI Training Grant T32 CA118681. The REDCap platform services are made possible by Stanford School of Medicine Research Office. The REDCap platform services at Stanford are subsidized by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085. The data content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
S.-m.P., D.D.W., B.J.L., S.B., D.E. and S.S.G. are co-inventors of a patent application filed by Stanford University on the subject of this work (US patent application number 62/695326). S.S.G. is a consultant or receives funding from several companies that work in the healthcare space although none of these companies are directly involved in the current work.
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Park, Sm., Won, D.D., Lee, B.J. et al. A mountable toilet system for personalized health monitoring via the analysis of excreta. Nat Biomed Eng 4, 624–635 (2020). https://doi.org/10.1038/s41551-020-0534-9
Nature Biomedical Engineering (2020)
Physics of Fluids (2020)
International Journal of Environmental Research and Public Health (2020)
ACS Infectious Diseases (2020)