Babar Zeb
ABSTRACT
Urinalysis is a significant technique used for determining and inspecting the urinary system. Urine has numerous chemical materials secreted; these materials can be used to diagnose diseases and conditions such as urinary tract infection, diabetes, kidney diseases, and pregnancy, at the earliest. Accessing medical care and health screenings are quite essential, but it has become extremely difficult since screening techniques are either very expensive or convoluted for people of low-income communities. Despite the fact, that numerous urinalysis methods and techniques have been brought forth by researchers over the years, no research has been conducted to scrutinize and review state-of-the-art developments in the stated area. Hence, this research aims to conduct a Systematic Literature Review of urinalysis methods proposed and assessed from 2007 to 2019 and recognizes 33 studies. This leads to the identification of 10 methods, 8 technologies, 12 challenges, and 10 diseases. An insight analysis of the identified methods and models reveals that Genetic Based Fuzzy Classifying Method and Automatic Urinary Particle Recognition Method are the most optimum options due to the fact that their computational time is minimum as well as they do not require any supportive hardware. Moreover, the analysis of technologies reveals that Mobile App (Augmented Reality) is the best option among the identified technologies. It has also been discovered that machine/deep learning classification techniques have been used to classify the sample and provide reliable and accurate results within time. Nevertheless, a complete analysis of methods and technologies has been presented. The findings of this article are extremely useful for practitioners as well as academics of the area.
- P. Wu, M. Zhu, P. Pu and T. Jiang, "An Adaptive Sampling Ensemble Learning Method for Urinalysis Model", 2010 2nd International Conference on Information Engineering and Computer Science, 2010. Available: 10.1109/iciecs.2010.5678258 [Accessed 21 July 2019].Google Scholar
Cross Ref
- R. Ginardi, A. Saikhu, R. Sarno, D. Sunaryono, A. Kholimi and R. Shanty, "Intelligent Method for Dipstick Urinalysis Using Smartphone Camera", Information and Communication Technology, pp. 66--77, 2014. Available: 10.1007/978-3-642-55032-4_7 [Accessed 23 July 2019].Google ScholarCross Ref
- F. de Boer et al., "Accurate and fast urinalysis in febrile patients by flow cytometry", Infectious Diseases, vol. 49, no. 5, pp. 380--387, 2017. Available: 10.1080/23744235.2016.1274048 [Accessed 26 July 2019].Google ScholarCross Ref
- C. Ongkum, K. Keawmitr and E. Boonchieng, "Analysis system for urine strip test using image processing technique", 2016 9th Biomedical Engineering International Conference (BMEiCON), 2016. Available: 10.1109/bmeicon.2016.7859610 [Accessed 22 July 2019].Google ScholarCross Ref
- N. Arnett, A. Vergani, A. Winkler, S. Ritter and K. Mehta, "Inexpensive urinalysis test strips to screen for diabetes in developing countries", 2016 IEEE Global Humanitarian Technology Conference (GHTC), 2016. Available: 10.1109/ghtc.2016.7857339 [Accessed 22 July 2019].Google ScholarCross Ref
- N. Frazzette, J. Dobson, A. Mukhtar and B. Burt, "Can we manufacture diagnostic test strips using an Inkjet printer?", 2015 IEEE Global Humanitarian Technology Conference (GHTC), 2015. Available: 10.1109/ghtc.2015.7344008 [Accessed 25 July 2019].Google ScholarCross Ref
- J. Ro et al., "Development of a Plural Colorimeter Module for Urinalysis Strip Readers", 2007 International Conference on Convergence Information Technology (ICCIT 2007), 2007. Available: 10.1109/iccit.2007.411 [Accessed 25 July 2019].Google Scholar
- G. Gundermann, S. Sen, S. Ritter and K. Mehta, "Stamping: A low-cost manufacturing method to deposit assays", 2016 IEEE Global Humanitarian Technology Conference (GHTC), 2016. Available: 10.1109/ghtc.2016.7857341 [Accessed 25 July 2019].Google ScholarCross Ref
- M. Velikova, P. Lucas, R. Smeets and J. Terwisscha van Scheltinga, "Fully-automated interpretation of biochemical tests for decision support by smartphones", 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS), 2012. Available: 10.1109/cbms.2012.6266352 [Accessed 25 July 2019].Google Scholar
- P. Wu, E. Goodman, T. Jiang and M. Pei, "A hybrid GA-based fuzzy classifying approach to urinary analysis modeling", Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09, 2009. Available: 10.1145/1570256.1570381 [Accessed 27 July 2019].Google Scholar
- Y. Liang, R. Kang, C. Lian and Y. Mao, "An End-to-End System for Automatic Urinary Particle Recognition with Convolutional Neural Network", Journal of Medical Systems, vol. 42, no. 9, 2018. Available: 10.1007/s10916-018-1014-6 [Accessed 27 July 2019].Google ScholarDigital Library
- Y. Zhou and H. Zhou, "Automatic Classification and Recognition of Particles in Urinary Sediment Images", Lecture Notes in Electrical Engineering, pp. 1071--1078, 2011. Available: 10.1007/978-94-007-1839-5_116 [Accessed 27 July 2019].Google Scholar
- P. Andreini, S. Bonechi, M. Bianchini, A. Mecocci and V. Di Massa, "Automatic Image Classification for the Urinoculture Screening", Intelligent Decision Technologies, pp. 31--42, 2015. Available: 10.1007/978-3-319-19857-6_4 [Accessed 27 July 2019].Google ScholarCross Ref
- R. Fletcher, N. Pignatelli, A. Jimenez-Galindo and S. Ghosh-Jerath, "Development of smart phone tools for printed diagnostics: Challenges and solutions", 2016 IEEE Global Humanitarian Technology Conference (GHTC), 2016. Available: 10.1109/ghtc.2016.7857355 [Accessed 22 July 2019].Google ScholarCross Ref
- K. Konnaiyan, S. Cheemalapati, M. Gubanov and A. Pyayt, "mHealth Dipstick Analyzer for Monitoring of Pregnancy Complications", IEEE Sensors Journal, vol. 17, no. 22, pp. 7311--7316, 2017. Available: 10.1109/jsen.2017.2752722 [Accessed 23 July 2019].Google ScholarCross Ref
- J. Neumeyer, J. Prince, A. Miller, B. Koeneman, S. Figueria and U. Kim, "Mobile urinalysis for maternal screening: Frugal medical screening solution and patient database to aid in prenatal healthcare for expecting mothers in the developing world", 2016 IEEE Global Humanitarian Technology Conference (GHTC), 2016. Available: 10.1109/ghtc.2016.7857337 [Accessed 23 July 2019].Google ScholarCross Ref
- R. Sicat, G. Tangonan and M. Guico, "Patient-Centric Medical Database with Remote Urinalysis Test", 2009 WRI World Congress on Computer Science and Information Engineering, 2009. Available: 10.1109/csie.2009.512 [Accessed 23 July 2019].Google ScholarDigital Library
- D. Lee, W. Ick Jang and M. Yeon Jung, "A pocket-sized colorimetric urine reader for telemedicine in the developing countries", 2011 IEEE SENSORS Proceedings, 2011. Available: 10.1109/icsens.2011.6127234 [Accessed 21 July 2019].Google ScholarCross Ref
- J. Bekhof, B. Kollen, L. Groot-Jebbink, C. Deiman, S. van de Leur and H. van Straaten, "Validity and interobserver agreement of reagent strips for measurement of glucosuria", Scandinavian Journal of Clinical and Laboratory Investigation, vol. 71, no. 3, pp. 248--252, 2011. Available: 10.3109/00365513.2011.558109 [Accessed 27 July 2019].Google ScholarCross Ref
- M. Ra, M. Muhammad, C. Lim, S. Han, C. Jung and W. Kim, "Smartphone-Based Point-of-Care Urinalysis Under Variable Illumination", IEEE Journal of Translational Engineering in Health and Medicine, vol. 6, pp. 1--11, 2018. Available: 10.1109/jtehm.2017.2765631 [Accessed 25 July 2019].Google ScholarCross Ref
- G. Budianto, T. Harsono and H. Yuniarti, "Strip Test Analysis Using Image Processing for Diagnosing Diabetes and Kidney Stone Based on Smartphone", 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), 2018. Available: 10.1109/kcic.2018.8628569 [Accessed 25 July 2019].Google ScholarCross Ref
- S. Mohamed, "UCHEK: an affordable smartphone based point of care diagnostic system for a low resource medical setup", Appropriate Healthcare Technologies for Low Resource Settings (AHT 2014), 2014. Available: 10.1049/cp.2014.0783 [Accessed 25 July 2019].Google ScholarCross Ref
- C. Florkowski, A. Don-Wauchope, N. Gimenez, K. Rodriguez-Capote, J. Wils and A. Zemlin, "Point-of-care testing (POCT) and evidence-based laboratory medicine (EBLM) -- does it leverage any advantage in clinical decision making?", Critical Reviews in Clinical Laboratory Sciences, vol. 54, no. 7-8, pp. 471--494, 2017. Available: 10.1080/10408363.2017.1399336 [Accessed 27 July 2019].Google ScholarCross Ref
- S. Ahn, K. Lee, D. Kim and V. Joseph, "Ubiquitous Healthcare Architecture Using SmartBidet and HomeServer with Embedded Urinalysis Agent", Ubiquitous Intelligence and Computing, pp. 205--213. Available: 10.1007/978-3-540-73549-6_21 [Accessed 27 July 2019].Google Scholar
- C. Naugler and D. Church, "Automation and artificial intelligence in the clinical laboratory", Critical Reviews in Clinical Laboratory Sciences, vol. 56, no. 2, pp. 98--110, 2019. Available: 10.1080/10408363.2018.1561640 [Accessed 26 July 2019].Google ScholarCross Ref
- D. Kocer, F. Sarıguzel and C. Karakukcu, "Cutoff values for bacteria and leukocytes for urine sediment analyzer FUS200 in culture-positive urinary-tract infections", Scandinavian Journal of Clinical and Laboratory Investigation, vol. 74, no. 5, pp. 414--417, 2014. Available: 10.3109/00365513.2014.900189 [Accessed 26 July 2019].Google ScholarCross Ref
- D. Bren-Cardali, L. Dao, J. Destruel, R. Fernandez, S. Figueira and U. Kim, "Urinalysis Screening for Rural Communities", 2018 IEEE Global Humanitarian Technology Conference (GHTC), 2018. Available: 10.1109/ghtc.2018.8601586 [Accessed 23 July 2019].Google ScholarCross Ref
- M. Beňovská, O. Wiewiorka and J. Pinkavová, "Evaluation of FUS-2000 urine analyzer: analytical properties and particle recognition", Scandinavian Journal of Clinical and Laboratory Investigation, vol. 78, no. 1-2, pp. 143--148, 2018. Available: 10.1080/00365513.2017.1423108 [Accessed 26 July 2019].Google ScholarCross Ref
- N. Tachpetpaiboon, K. Kularbphettong and S. Janpla, "Expert System for Diagnosing Disease Risk from Urine Tests", Advances in Intelligent Systems and Computing, pp. 241--248, 2018. Available: 10.1007/978-981-13-0344-9_20 [Accessed 27 July 2019].Google Scholar
- A. Bertao and T. Dong, "Stability of colorimetric results in the detection of urine biomarkers using a paper-based analytical device", 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017. Available: 10.1109/embc.2017.8036793 [Accessed 23 July 2019].Google ScholarCross Ref
- M. Fritzenwanker, C. Imirzalioglu, T. Chakraborty and F. Wagenlehner, "Modern diagnostic methods for urinary tract infections", Expert Review of Anti-infective Therapy, vol. 14, no. 11, pp. 1047--1063, 2016. Available: 10.1080/14787210.2016.1236685 [Accessed 26 July 2019].Google ScholarCross Ref
- K. Shinozawa, N. Hagita, M. Furutani and R. Matsuoka, "A Data Mining Method for Finding Hidden Relationship in Blood and Urine Examination Items for Health Check", Advances in Data Mining. Applications and Theoretical Aspects, pp. 44--50, 2009. Available: 10.1007/978-3-642-03067-3_5 [Accessed 27 July 2019].Google ScholarDigital Library
- J. Delanghe, "NEW SCREENING DIAGNOSTIC TECHNIQUES IN URINALYSIS", Acta Clinica Belgica, vol. 62, no. 3, pp. 155--161, 2007. Available: 10.1179/acb.2007.026 [Accessed 26 July 2019].Google ScholarCross Ref
Index Terms
- Towards the Selection of the Best Machine Learning Techniques and Methods for Urinalysis
Recommendations
Application of Electronic Nose for Diagnosing Azotemia from Urinalysis Using Principal Component Analysis
ICBET '20: Proceedings of the 2020 10th International Conference on Biomedical Engineering and TechnologyAzotemia is a disease that is easy to be treated, but when left undiagnosed and untreated could lead to more fatal diseases like those of Kidney failure or Renal Diseases. Azotemia is a common disease; however, the lack of information from urinalysis ...
Measurement of Specific Gravity, Urobilinogen, Blood, Protein and pH Level of Urine Samples Using Raspberry Pi based Portable Urine Test Strip Analyzer
ICBET '20: Proceedings of the 2020 10th International Conference on Biomedical Engineering and TechnologyUrinalysis has been a helpful tool in detecting some common diseases. It has been used to diagnose conditions such as urinary tract infections, kidney disorders, liver problems, and other metabolic conditions. Regular urinalysis may be done to detect ...
Patient-Centric Medical Database with Remote Urinalysis Test
CSIE '09: Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 06The patient-centric medical database called ECMED (EleCtronic MEdical Database) is a patient owned repository for medical records which is made accessible via the Internet and/or the GSM network (using low cost mobile phones). The database stores ...
Comments