한빛사 논문
Seung-min Park 1,2,17, Daeyoun D. Won1,3,4,17, Brian J. Lee1,2,17, Diego Escobedo1, Andre Esteva5, Amin Aalipour1,2, T. Jessie Ge6, Jung Ha Kim3, Susie Suh7, Elliot H. Choi7, Alexander X. Lozano 8,9, 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 and Sanjiv S. Gambhir 1,2,8,11,15,16 ,*
1Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
2Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA.
3Department of Surgery, Seoul Song Do Hospital, Seoul, Republic of Korea.
4Cancer Immunology Laboratory, Seoul, Seoul Song Do Hospital, Republic of Korea.
5Salesforce Research, Palo Alto, CA, USA.
6Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
7Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
8Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
9Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
10Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
11Department of Bioengineering, Stanford University, Stanford, CA, USA.
12Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands.
13Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
14College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
15Precision Health and Integrated Diagnostic Center (PHIND), Stanford University School of Medicine, Palo Alto, CA, USA.
16Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA.
17These authors contributed equally: Seung-min Park, Daeyoun D. Won, Brian J. Lee.
*Corresponding author
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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