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

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

This article has been updated

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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).

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.

Change history

  • 07 May 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Porche, D. J. Precision medicine initiative. Am. J. Mens Health 9, 177 (2015).

    PubMed  Article  Google Scholar 

  2. 2.

    Ashley, E. A. The precision medicine initiative: a new national effort. JAMA 313, 2119–2120 (2015).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    What is precision medicine? Genetics Home Reference https://ghr.nlm.nih.gov/primer/precisionmedicine/definition (2020).

  5. 5.

    Gambhir, S. S., Ge, T. J., Vermesh, O. & Spitler, R. Toward achieving precision health. Sci. Transl. Med. 10, eaao3612 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Blackwell, D. L., Lucas, J. W. & Clarke, T. C. Summary health statistics for U.S. adults: national health interview survey, 2012. Vital Health Stat. 10, 1–161 (2014).

    Google Scholar 

  7. 7.

    Aalipour, A. et al. Deactivated CRISPR associated protein 9 for minor-allele enrichment in cell-free DNA. Clin. Chem. 64, 307–316 (2018).

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Park, S.-m et al. Molecular profiling of single circulating tumor cells from lung cancer patients. Proc. Natl Acad. Sci. USA 113, E8379–E8386 (2016).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Krilaviciute, A. et al. Detection of cancer through exhaled breath: a systematic review. Oncotarget 6, 38643–38657 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Peng, G. et al. Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors. Br. J. Cancer 103, 542–551 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Hong, Y. J. et al. Multifunctional wearable system that integrates sweat‐based sensing and vital‐sign monitoring to estimate pre‐/post‐exercise glucose levels. Adv. Funct. Mater. 28, 1805754 (2018).

    Article  CAS  Google Scholar 

  12. 12.

    Bandodkar, A. J. et al. Battery-free, skin-interfaced microfluidic/electronic systems for simultaneous electrochemical, colorimetric, and volumetric analysis of sweat. Sci. Adv. 5, eaav3294 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. 13.

    Lee, J.-R., Choi, J., Shultz, T. O. & Wang, S. X. Small molecule detection in saliva facilitates portable tests of marijuana abuse. Anal. Chem. 88, 7457–7461 (2016).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Kim, J. et al. Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites. Analyst 139, 1632–1636 (2014).

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Harpole, M., Davis, J. & Espina, V. Current state of the art for enhancing urine biomarker discovery. Expert Rev. Proteom. 13, 609–626 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Decramer, S. et al. Urine in clinical proteomics. Mol. Cell. Proteom. 7, 1850–1862 (2008).

    CAS  Article  Google Scholar 

  17. 17.

    Davies, R. J., Miller, R. & Coleman, N. Colorectal cancer screening: prospects for molecular stool analysis. Nat. Rev. Cancer 5, 199–209 (2005).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Gisbert, J. P., de la Morena, F. & Abraira, V. Accuracy of monoclonal stool antigen test for the diagnosis of H. pylori infection: a systematic review and meta-analysis. Am. J. Gastroenterol. 101, 1921–1930 (2006).

    PubMed  Article  Google Scholar 

  19. 19.

    Warren, A. D., Kwong, G. A., Wood, D. K., Lin, K. Y. & Bhatia, S. N. Point-of-care diagnostics for noncommunicable diseases using synthetic urinary biomarkers and paper microfluidics. Proc. Natl Acad. Sci. USA 111, 3671–3676 (2014).

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Lin, K. Y., Kwong, G. A., Warren, A. D., Wood, D. K. & Bhatia, S. N. Nanoparticles that sense thrombin activity as synthetic urinary biomarkers of thrombosis. ACS Nano 7, 9001–9009 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21.

    Graff, L. A Handbook of Routine Urinalysis (Lippincott, 1983).

  22. 22.

    Simerville, J. A., Maxted, W. C. & Pahira, J. J. Urinalysis: a comprehensive review. Am. Fam. Physician 71, 1153–1162 (2005).

    PubMed  Google Scholar 

  23. 23.

    Schäfer, W. et al. Good urodynamic practices: uroflowmetry, filling cystometry, and pressure‐flow studies. Neurourol. Urodyn. 21, 261–274 (2002).

    PubMed  Article  Google Scholar 

  24. 24.

    Dabhoiwala, N., Osawa, D., Lim, A. T. L. & Abrams, P. The ICS-‘BPH’ study: uroflowmetry, lower urinary tract symptoms and bladder outlet obstruction. Br. J. Urol. 82, 619–623 (1998).

    PubMed  Article  Google Scholar 

  25. 25.

    Jørgensen, J. B., Jensen, K. E., Bille‐Brahe, N. & Mogensen, P. Uroflowmetry in asymptomatic elderly males. BJU Int. 58, 390–395 (1986).

    Article  Google Scholar 

  26. 26.

    El Din, K. E., Kiemeney, L., De Wildt, M., Debruyne, F. & de La Rosette, J. Correlation between uroflowmetry, prostate volume, postvoid residue, and lower urinary tract symptoms as measured by the International Prostate Symptom Score. Urology 48, 393–397 (1996).

    Article  Google Scholar 

  27. 27.

    Markland, A. D. et al. Association of low dietary intake of fiber and liquids with constipation: evidence from the National Health and Nutrition Examination Survey. Am. J. Gastroenterol. 108, 796–803 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Li, B. R. et al. Risk factors for steatorrhea in chronic pancreatitis: a cohort of 2,153 patients. Sci. Rep. 6, 21381 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Johnson, C. D. et al. Qualitative assessment of the symptoms and impact of pancreatic exocrine insufficiency (PEI) to inform the development of a patient-reported outcome (PRO) instrument. Patient 10, 615–628 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Parekh, D. & Natarajan, S. Surgical management of chronic pancreatitis. Indian J. Surg. 77, 453–469 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Shandro, B. M., Nagarajah, R. & Poullis, A. Challenges in the management of pancreatic exocrine insufficiency. World J. Gastrointest. Pharm. Ther. 9, 39–46 (2018).

    Article  Google Scholar 

  32. 32.

    de la Iglesia-Garcia, D. et al. Efficacy of pancreatic enzyme replacement therapy in chronic pancreatitis: systematic review and meta-analysis. Gut 66, 1354–1355 (2017).

    PubMed  Google Scholar 

  33. 33.

    Park, H. H., Kim, H. Y., Jung, S. E., Lee, S. C. & Park, K. W. Long-term functional outcomes of PPPD in children—nutritional status, pancreatic function, GI function and QOL. J. Pediatr. Surg. 51, 398–402 (2016).

    PubMed  Article  Google Scholar 

  34. 34.

    D’haens, G. et al. Fecal calprotectin is a surrogate marker for endoscopic lesions in inflammatory bowel disease. Inflamm. Bowel Dis. 18, 2218–2224 (2012).

    PubMed  Article  Google Scholar 

  35. 35.

    Van Rheenen, P. F., Van de Vijver, E. & Fidler, V. Faecal calprotectin for screening of patients with suspected inflammatory bowel disease: diagnostic meta-analysis. Brit. Med. J. 341, c3369 (2010).

    PubMed  Article  Google Scholar 

  36. 36.

    Schoepfer, A. M. et al. Fecal calprotectin correlates more closely with the Simple Endoscopic Score for Crohn’s disease (SES-CD) than CRP, blood leukocytes, and the CDAI. Am. J. Gastroenterol. 105, 162–169 (2010).

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    Imperiale, T. F. et al. Multitarget stool DNA testing for colorectal-cancer screening. N. Engl. J. Med. 370, 1287–1297 (2014).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Ahlquist, D. A. et al. Next-generation stool DNA test accurately detects colorectal cancer and large adenomas. Gastroenterology 142, 248–256 (2012).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Lane, M. M., Czyzewski, D. I., Chumpitazi, B. P. & Shulman, R. J. Reliability and validity of a modified Bristol Stool Form Scale for children. J. Pediatr. 159, 437–441 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Candy, D. & Edwards, D. The management of chronic constipation. Curr. Paediatr. 13, 101–106 (2003).

    Article  Google Scholar 

  41. 41.

    Halmos, E. P. et al. Inaccuracy of patient-reported descriptions of and satisfaction with bowel actions in irritable bowel syndrome. Neurogastroenterol. Motil. 30, e13187 (2018).

    Article  CAS  Google Scholar 

  42. 42.

    Mínguez, P. M. & Benages, M. A. The Bristol scale—a useful system to assess stool form? Rev. Esp. Enferm. Dig. 101, 305–311 (2009).

    Google Scholar 

  43. 43.

    Ikenaga, T., Shigematsu, T., Kusumoto, A., Yamamoto, K. & Yada, M. Toilet device with health examination system. US patent US4961431A (1990).

  44. 44.

    Ikenaga, T., Shigematsu, T., Yada, M., Makita, S. & Kitaura, H. Toilet with urine constituent measuring device. US patent US4962550A (1990).

  45. 45.

    Nakayama, C. et al. Toilet-bowl-mounted urinalysis unit. US patent US5730149A (1998).

  46. 46.

    Voswinckel, P. A marvel of colors and ingredients. The story of urine test strip. Kidney Int. Suppl. 47, S3–S7 (1994).

    CAS  PubMed  Google Scholar 

  47. 47.

    Yang, P. J., Pham, J., Choo, J. & Hu, D. L. Duration of urination does not change with body size. Proc. Natl Acad. Sci. USA 111, 11932–11937 (2014).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Haylen, B. T., Ashby, D., Sutherst, J. R., Frazer, M. I. & West, C. R. Maximum and average urine flow rates in normal male and female populations—the Liverpool nomograms. Br. J. Urol. 64, 30–38 (1989).

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Kim, J. H. et al. Terminal dribbling in male patients with lower urinary tract symptoms: relationship with international prostate symptom score and with intravesical prostatic protrusion. BMC Urol. 15, 89 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. 50.

    Jurman, G., Riccadonna, S. & Furlanello, C. A comparison of MCC and CEN error measures in multi-class prediction. PLoS ONE 7, e41882 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Gorodkin, J. Comparing two K-category assignments by a K-category correlation coefficient. Comput. Biol. Chem. 28, 367–374 (2004).

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    Wei, J.-M., Yuan, X.-J., Hu, Q.-H. & Wang, S.-Q. A novel measure for evaluating classifiers. Expert Syst. Appl. 37, 3799–3809 (2010).

    Article  Google Scholar 

  53. 53.

    Blake, M., Raker, J. & Whelan, K. Validity and reliability of the Bristol stool form scale in healthy adults and patients with diarrhoea-predominant irritable bowel syndrome. Aliment. Pharmacol. Ther. 44, 693–703 (2016).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Krishnasamy, P., Belongie, S. & Kriegman, D. Wet fingerprint recognition: challenges and opportunities. In Proc. 2011 International Joint Conference on Biometrics 1–7 (IEEE, 2011).

  55. 55.

    Gonzalez, R. C. & Woods, R. E. Digital Image Processing (Addison-Wesley, 1992).

  56. 56.

    Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).

    PubMed  Article  Google Scholar 

  57. 57.

    Lewis, S. J. & Heaton, K. W. Stool form scale as a useful guide to intestinal transit time. Scand. J. Gastroenterol. 32, 920–924 (1997).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    von Münch, E. & Milosevic, D. Qualitative Survey on Squatting Toilets and Anal Cleansing with Water with a Special Emphasis on Muslim and Buddhist Countries by Using the SuSanA Discussion Forum (Ostella Consulting, 2015).

  60. 60.

    Krhut, J. et al. Comparison between uroflowmetry and sonouroflowmetry in recording of urinary flow in healthy men. Int J. Urol. 22, 761–765 (2015).

    PubMed  Article  Google Scholar 

  61. 61.

    Yang, P. J., LaMarca, M., Kaminski, C., Chu, D. I. & Hu, D. L. Hydrodynamics of defecation. Soft Matter 13, 4960–4970 (2017).

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Van Rossum, L. G. et al. Random comparison of guaiac and immunochemical fecal occult blood tests for colorectal cancer in a screening population. Gastroenterology 135, 82–90 (2008).

    PubMed  Article  Google Scholar 

  63. 63.

    Morikawa, T. et al. A comparison of the immunochemical fecal occult blood test and total colonoscopy in the asymptomatic population. Gastroenterology 129, 422–428 (2005).

    PubMed  Article  Google Scholar 

  64. 64.

    Guittet, L. et al. Comparison of a guaiac based and an immunochemical faecal occult blood test in screening for colorectal cancer in a general average risk population. Gut 56, 210–214 (2007).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Raman, M. et al. Fecal microbiome and volatile organic compound metabolome in obese humans with nonalcoholic fatty liver disease. Clin. Gastroenterol. Hepatol. 11, 868–875 (2013).

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Damman, C. J., Miller, S. I., Surawicz, C. M. & Zisman, T. L. The microbiome and inflammatory bowel disease: is there a therapeutic role for fecal microbiota transplantation? Am. J. Gastroenterol. 107, 1452–1459 (2012).

    PubMed  Article  Google Scholar 

  67. 67.

    Ley, R. E. Obesity and the human microbiome. Curr. Opin. Gastroenterol. 26, 5–11 (2010).

    PubMed  Article  Google Scholar 

  68. 68.

    Chang, J. Y. et al. Decreased diversity of the fecal microbiome in recurrent Clostridium difficile—associated diarrhea. J. Infect. Dis. 197, 435–438 (2008).

    PubMed  Article  Google Scholar 

  69. 69.

    Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Proc. 25th International Conference on Neural Information Processing Systems — Volume 1 1097–1105 (Curran Associates, 2012).

  70. 70.

    Abadi, M. et al. Tensorflow: a system for large-scale machine learning. In Proc. 12th USENIX Symposium on Operating Systems Design and Implementation 265–283 (USENIX, 2016).

Download references

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.

Author information

Affiliations

Authors

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.

Ethics declarations

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary methods, figures, tables and references.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing