دورية أكاديمية
A mountable toilet system for personalized health monitoring via the analysis of excreta.
| العنوان: | A mountable toilet system for personalized health monitoring via the analysis of excreta. |
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| المؤلفون: | Park SM; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.; Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA., Won DD; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.; Department of Surgery, Seoul Song Do Hospital, Seoul, Republic of Korea.; Cancer Immunology Laboratory, Seoul, Seoul Song Do Hospital, Republic of Korea., Lee BJ; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.; Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA., Escobedo D; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA., Esteva A; Salesforce Research, Palo Alto, CA, USA., Aalipour A; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.; Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA., Ge TJ; Department of Urology, Stanford University School of Medicine, Stanford, CA, USA., Kim JH; Department of Surgery, Seoul Song Do Hospital, Seoul, Republic of Korea., Suh S; Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH, USA., Choi EH; Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH, USA., Lozano AX; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada., Yao C; Department of Electrical Engineering, Stanford University, Stanford, CA, USA., Bodapati S; Department of Bioengineering, Stanford University, Stanford, CA, USA., Achterberg FB; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.; Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA.; Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands., Kim J; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.; Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA.; Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea., Park H; College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea., Choi Y; College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea., Kim WJ; College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea., Yu JH; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.; Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA., Bhatt AM; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA., Lee JK; Department of Surgery, Seoul Song Do Hospital, Seoul, Republic of Korea.; Cancer Immunology Laboratory, Seoul, Seoul Song Do Hospital, Republic of Korea., Spitler R; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.; Precision Health and Integrated Diagnostic Center (PHIND), Stanford University School of Medicine, Palo Alto, CA, USA., Wang SX; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA., Gambhir SS; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. sgambhir@stanford.edu.; Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA. sgambhir@stanford.edu.; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA. sgambhir@stanford.edu.; Department of Bioengineering, Stanford University, Stanford, CA, USA. sgambhir@stanford.edu.; Precision Health and Integrated Diagnostic Center (PHIND), Stanford University School of Medicine, Palo Alto, CA, USA. sgambhir@stanford.edu.; Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA. sgambhir@stanford.edu. |
| المصدر: | Nature biomedical engineering [Nat Biomed Eng] 2020 Jun; Vol. 4 (6), pp. 624-635. Date of Electronic Publication: 2020 Apr 06. |
| نوع المنشور: | Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Nature Country of Publication: England NLM ID: 101696896 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2157-846X (Electronic) Linking ISSN: 2157846X NLM ISO Abbreviation: Nat Biomed Eng Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: London : Springer Nature Original Publication: [London] : Macmillan Publishers Limited, [2016]- |
| مواضيع طبية MeSH: | Bathroom Equipment* , Equipment Design*, Monitoring, Physiologic/*instrumentation , Monitoring, Physiologic/*methods, Adult ; Deep Learning ; Feces/chemistry ; Female ; Humans ; Male ; Signal Processing, Computer-Assisted ; Software ; Urine/chemistry ; User-Computer Interface |
| مستخلص: | 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. |
| التعليقات: | Erratum in: Nat Biomed Eng. 2020 May 7;:. (PMID: 32382068) Comment in: Nat Rev Gastroenterol Hepatol. 2020 Aug;17(8):453-454. (PMID: 32483351) Comment in: Nat Biomed Eng. 2020 Jun;4(6):581-582. (PMID: 32533122) |
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| معلومات مُعتمدة: | T32 CA118681 United States CA NCI NIH HHS; T32 GM007250 United States GM NIGMS NIH HHS; UL1 TR001085 United States TR NCATS NIH HHS; UL1 TR003142 United States TR NCATS NIH HHS |
| تواريخ الأحداث: | Date Created: 20200407 Date Completed: 20201112 Latest Revision: 20220304 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC7377213 |
| DOI: | 10.1038/s41551-020-0534-9 |
| PMID: | 32251391 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 2157-846X |
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| DOI: | 10.1038/s41551-020-0534-9 |