4.7 Article

Smartphone-Based Rapid Screening of Urinary Biomarkers

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBCAS.2016.2633508

关键词

Colorimetry; discriminant analysis; image processing; mobile devices; urinalysis

资金

  1. Oslofjordfond project Touchsensor for enklere og raskere urinprovetaking og analyse [234972]
  2. Oslofjordfond project Papirbasert kolorimetrisk sensorsystem med integrert polymer-lyskilder og -detektorer for kvantitativ deteksjon av biomarkorer i spyt [249017]
  3. Oslofjordfond project Smart-toy for eldreomsorg: Oppfolging av fysiske aktiviteter og overvakning av fysiologisk status i sanntid [260586]
  4. Forprosjekt-VRIBEDRIFT Vestfold mikrofluidikkbasert PCR med kontinuerlig stromning i sanntid, for rask deteksjon av mikrobielle patogener i vann [252173]
  5. Chongqing Research Program of Basic Research and Frontier Technology [cstc2016jcyjA2161, cstc2015jcyjA20023]
  6. National Natural Science Foundation of China [61550110253, 61650410655]
  7. Science and Technology Research Program of Chongqing Education Commission [KJ1600604]
  8. Chongqing Innovation Team of Colleges and Universities [CXTDX201601025]

向作者/读者索取更多资源

An ambulatory pre-screening Point-of-Care (POC) device compatible with commercially available diapers has been developed to rapidly screen urine samples for incontinent or functionally impaired elderly. This POC device consists of a set of colorimetric reaction pads with accompanying reference colors. A smartphone with camera is a convenient tool for analysis of colorimetric assays; and a software application has been developed for smartphones to photograph the colorimetric assay and classify colorimetric reactions according to the reference colors. To facilitate detection of multiple biomarkers, e.g., 12 biomarkers with 2-7 references per biomarker, automatic localization of test/reference pads has been implemented through recognition of corner alignment marks and projective coordinate transformation for perspective removal. Each test run trains a set of classifiers from extracted reference data, which is used to classify the extracted test data. The smartphone application gives semi-quantitative results and functions independently of illumination intensity, illumination color, device type (smartphone brand/model), device settings (ISO, shutter speed, aperture) and automatic camera preprocessing. The smartphone application has been tested successfully on Samsung Galaxy S3, S6 Edge, S7 Edge, ZTE Nubia V7 mini and Iphone 6 in various illumination conditions.

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