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A mountable toilet system for personalized health monitoring via the analysis of excreta

Nature Biomedical Engineering volume 4, pages 624–635 (2020)Cite this article

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A Publisher Correction to this article was published on 07 May 2020

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Abstract

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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Fig. 1: Schematic of the toilet system.
Fig. 2: Computer-vision urinalysis and uroflowmetry of the toilet system.
Fig. 3: CNN for stool analysis.
Fig. 4: Defecation monitoring module of the toilet system.
Fig. 5: Biometric identifications using the fingerprint and the anal creases (the distinctive features of anoderm, or analprint).

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Data availability

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.

Code availability

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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Acknowledgements

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.

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Author notes

  1. These authors contributed equally: Seung-min Park, Daeyoun D. Won, Brian J. Lee.

Authors and Affiliations

  1. Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA

    Seung-min Park, Daeyoun D. Won, Brian J. Lee, Diego Escobedo, Amin Aalipour, Friso B. Achterberg, Jeesu Kim, Jung Ho Yu, Alexander M. Bhatt, Ryan Spitler & Sanjiv S. Gambhir

  2. Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA

    Seung-min Park, Brian J. Lee, Amin Aalipour, Friso B. Achterberg, Jeesu Kim, Jung Ho Yu & Sanjiv S. Gambhir

  3. Department of Surgery, Seoul Song Do Hospital, Seoul, Republic of Korea

    Daeyoun D. Won, Jung Ha Kim & Jong Kyun Lee

  4. Cancer Immunology Laboratory, Seoul, Seoul Song Do Hospital, Republic of Korea

    Daeyoun D. Won & Jong Kyun Lee

  5. Salesforce Research, Palo Alto, CA, USA

    Andre Esteva

  6. Department of Urology, Stanford University School of Medicine, Stanford, CA, USA

    T. Jessie Ge

  7. Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Susie Suh & Elliot H. Choi

  8. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

    Alexander X. Lozano, Shan X. Wang & Sanjiv S. Gambhir

  9. Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada

    Alexander X. Lozano

  10. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

    Chengyang Yao & Shan X. Wang

  11. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Sunil Bodapati & Sanjiv S. Gambhir

  12. Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands

    Friso B. Achterberg

  13. Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

    Jeesu Kim

  14. College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

    Hwan Park, Youngjae Choi & Woo Jin Kim

  15. Precision Health and Integrated Diagnostic Center (PHIND), Stanford University School of Medicine, Palo Alto, CA, USA

    Ryan Spitler & Sanjiv S. Gambhir

  16. Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA

    Shan X. Wang & Sanjiv S. Gambhir

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Contributions

S.S.G. conceived the original idea of the toilet system. S.-m.P. and S.S.G. strategically prioritized various clinical applications. S.-m.P., D.D.W., B.J.L., D.E. and S.S.G. contributed to overall study design, product prototyping and data analysis. A.E. and A.X.L. contributed to the development of machine learning algorithm used in the toilet. B.J.L, J.K., T.J.G., S.B. and F.B.A. contributed to uroflowmetry module design and its execution. D.D.W., A.A., J.H.K, S.S., E.H.C, H.P., Y.C., W.J.K. and J.K.L. contributed to stool analysis. D.E., J.H.Y. and A.M.B contributed to urinalysis study design and module implementation. C.Y. and S.X.W. contributed to electronic circuit design, system automation, and overall modular development and integration of the system. S.-m.P., D.D.W., B.J.L., R.S. and S.S.G. analysed all of the data and wrote the paper.

Corresponding author

Correspondence to Sanjiv S. Gambhir.

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Competing interests

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

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