| Literature DB >> 32251391 |
Seung-Min Park1,2, Daeyoun D Won1,3,4, Brian J Lee1,2, Diego Escobedo1, Andre Esteva5, Amin Aalipour1,2, T Jessie Ge6, Jung Ha Kim3, Susie Suh7, Elliot H Choi7, Alexander X Lozano8,9, Chengyang Yao10, Sunil Bodapati11, Friso B Achterberg1,2,12, Jeesu Kim1,2,13, Hwan Park14, Youngjae Choi14, Woo Jin Kim14, Jung Ho Yu1,2, Alexander M Bhatt1, Jong Kyun Lee3,4, Ryan Spitler1,15, Shan X Wang8,10,16, Sanjiv S Gambhir17,18,19,20,21,22.
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.Entities:
Mesh：
Year: 2020 PMID： 32251391 PMCID： PMC7377213 DOI： 10.1038/s41551-020-0534-9
Source DB: PubMed Journal: Nat Biomed Eng ISSN： 2157-846X Impact factor: 25.671