skip to main content
10.1145/3411763.3451821acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
poster

RDTCheck: A Smartphone App for Monitoring Rapid Diagnostic Test Administration

Published: 08 May 2021 Publication History

Abstract

Rapid diagnostic tests are point-of-care medical tests that are used by clinicians and community healthcare workers to get quicker results at a better cost compared to traditional diagnostic tests. Distributing rapid diagnostic tests to people outside of the healthcare industry would significantly improve access to diagnostic testing; however, there are concerns that novices may administer rapid diagnostic tests incorrectly and thus be left with invalid results. In response to this concern, we propose RDTCheck — a mobile application that guides users through the instructions of Quidel’s QuickVue Influenza A+B test and ensures adherence to the procedure using computer vision. RDTCheck provides users with real-time feedback so that they may either correct their mistakes or re-administer their test. In this work, we conducted findings from a pilot study that demonstrates how well RDTCheck is able to detect common mistakes and successes during the various steps of the QuickVue test. For the 7 participants we recruited, RDTCheck had an average success rate of 91.1% at giving the correct feedback during the RDT administration procedure.

References

[1]
BankMyCell. 2020. How Many Smartphones Are In The World?, 26 pages. https://www.bankmycell.com/blog/how-many-phones-are-in-the-world
[2]
Roberto Brunelli. 2009. Template matching techniques in computer vision: theory and practice. John Wiley & Sons.
[3]
Scott Carter, John Adcock, John Doherty, and Stacy Branham. 2010. NudgeCam: Toward targeted, higher quality media capture. In MM’10 - Proceedings of the ACM Multimedia 2010 International Conference. 615–618. https://doi.org/10.1145/1873951.1874034
[4]
Helen Counihan, Steven A Harvey, Masela Sekeseke-Chinyama, Busiku Hamainza, Rose Banda, Thindo Malambo, Freddie Masaninga, and David Bell. 2012. Community health workers use malaria rapid diagnostic tests (RDTs) safely and accurately: results of a longitudinal study in Zambia. The American journal of tropical medicine and hygiene 87, 1 (2012), 57–63.
[5]
G. D. Evangelidis and E. Z. Psarakis. 2008. Parametric Image Alignment Using Enhanced Correlation Coefficient Maximization. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 10(2008), 1858–1865. https://doi.org/10.1109/TPAMI.2008.113
[6]
Ifeoma Jovita Ezennia, Sunday Odunke Nduka, and Obinna Ikechukwu Ekwunife. 2017. Cost benefit analysis of malaria rapid diagnostic test: the perspective of Nigerian community pharmacists. Malaria journal 16, 1 (2017), 1–10.
[7]
Nikolai Gorski, Valery Anisimov, Emmanuel Augustin, Olivier Baret, and Sergey Maximov. 2001. Industrial bank check processing: The A2iA CheckReader™. International Journal on Document Analysis and Recognition 3, 4(2001), 196–206. https://doi.org/10.1007/PL00013561
[8]
N. Gorski, V. Anisimov, E. Augustin, O. Baret, D. Price, and J. C. Simon. 1999. A2iA Check Reader: A family of bank check recognition systems. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR. IEEE Computer Society, 527–530. https://doi.org/10.1109/ICDAR.1999.791840
[9]
Steven A Harvey, Larissa Jennings, Masela Chinyama, Fred Masaninga, Kurt Mulholland, and David R Bell. 2008. Improving community health worker use of malaria rapid diagnostic tests in Zambia: package instructions, job aid and job aid-plus-training. Malaria journal 7, 1 (2008), 160.
[10]
Sócrates Herrera, Andrés F Vallejo, Juan P Quintero, Myriam Arévalo-Herrera, Marcela Cancino, and Santiago Ferro. 2014. Field evaluation of an automated RDT reader and data management device for Plasmodium falciparum/Plasmodium vivax malaria in endemic areas of Colombia. Malaria journal 13, 1 (2014), 1–10.
[11]
Berthold KP Horn and Brian G Schunck. 1981. Determining optical flow. Artificial intelligence 17, 1-3 (1981), 185–203.
[12]
L. Huette, P. Barbosa-Pereira, O. Bougeois, J. V. Moreau, B. Plessis, P. Courtellemont, and Y. LeCourtier. 1997. Multi-Bank Check Recognition System: Consideration on the Numeral Amount Recognition Module. 133–156. https://doi.org/10.1142/9789812797681_0006
[13]
Chandrika Jayant, Hanjie Ji, Samuel White, and Jeffrey P. Bigham. 2011. Supporting blind photography. In ASSETS’11: Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility. 203–210. https://doi.org/10.1145/2049536.2049573
[14]
A Lunežič, Tomáš Vojíř, L Čehovin Zajc, Jiří Matas, and Matej Kristan. 2018. Discriminative Correlation Filter Tracker with Channel and Spatial Reliability. International Journal of Computer Vision 126, 7 (2018), 671–688.
[15]
Victoria Lyon, Monica Zigman Suchsland, Monique Chilver, Nigel Stocks, Barry Lutz, Philip Su, Shawna Cooper, Chunjong Park, Libby Rose Lavitt, Alex Mariakakis, 2020. Diagnostic accuracy of an app-guided, self-administered test for influenza among individuals presenting to general practice with influenza-like illness: study protocol. BMJ open 10, 11 (2020), e036298.
[16]
Adesola Olalekan, Bamidele Iwalokun, Oluwabukola M Akinloye, Olayiwola Popoola, Titilola A Samuel, and Oluyemi Akinloye. 2020. COVID-19 rapid diagnostic test could contain transmission in low-and middle-income countries. African Journal of Laboratory Medicine 9, 1 (2020).
[17]
Nuttada Panpradist, Bhushan J Toley, Xiaohong Zhang, Samantha Byrnes, Joshua R Buser, Janet A Englund, and Barry R Lutz. 2014. Swab sample transfer for point-of-care diagnostics: characterization of swab types and manual agitation methods. PloS one 9, 9 (2014), e105786.
[18]
Chunjong Park, Alex Mariakakis, Jane Yang, Diego Lassala, Yasamba Djiguiba, Youssouf Keita, Hawa Diarra, Beatrice Wasunna, Fatou Fall, Marème Soda Gaye, 2020. Supporting Smartphone-Based Image Capture of Rapid Diagnostic Tests in Low-Resource Settings. In Proceedings of the 2020 International Conference on Information and Communication Technologies and Development. 1–11.
[19]
Thomas F Scherr, Sparsh Gupta, David W Wright, and Frederick R Haselton. 2016. Mobile phone imaging and cloud-based analysis for standardized malaria detection and reporting. Scientific reports 6(2016), 28645.
[20]
Osama ME Seidahmed, Muneir MN Mohamedein, Afrah A Elsir, Fayez T Ali, El Fatih M Malik, and Eldirdieri S Ahmed. 2008. End-user errors in applying two malaria rapid diagnostic tests in a remote area of Sudan. Tropical Medicine & International Health 13, 3 (2008), 406–409.
[21]
UN Medical Directors. 2020. COVID-19 Testing Recommendations for UN Personnel. Technical Report. United Nations. 1–5 pages. https://www.un.org/sites/un2.un.org/files/coronavirus_testingrecsforunpersonnelandcontingents.pdf
[22]
Samuel White, Hanjie Ji, and Jeffrey P. Bigham. 2010. EasySnap: Real-time audio feedback for blind photography. In UIST 2010 - 23rd ACM Symposium on User Interface Software and Technology, Adjunct Proceedings. 409–410. https://doi.org/10.1145/1866218.1866244
[23]
Joshua Yukich, Valerie D’Acremont, Judith Kahama, Ndeniria Swai, and Christian Lengeler. 2010. Cost savings with rapid diagnostic tests for malaria in low-transmission areas: evidence from Dar es Salaam, Tanzania. The American journal of tropical medicine and hygiene 83, 1 (2010), 61–68.

Cited By

View all
  • (2025)A novel guidance framework for nasal rapid antigen tests with improved swab keypoint detectionSmart Health10.1016/j.smhl.2024.10053435(100534)Online publication date: Mar-2025
  • (2022)Translating diagnostics and drug delivery technologies to low-resource settingsScience Translational Medicine10.1126/scitranslmed.abm173214:666Online publication date: 12-Oct-2022

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
May 2021
2965 pages
ISBN:9781450380959
DOI:10.1145/3411763
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Android application
  2. Computer Vision
  3. Rapid Diagnostic Tests

Qualifiers

  • Poster
  • Research
  • Refereed limited

Funding Sources

  • Bill & Melinda Gates Foundation

Conference

CHI '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025
ACM CHI Conference on Human Factors in Computing Systems
April 26 - May 1, 2025
Yokohama , Japan

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)16
  • Downloads (Last 6 weeks)1
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)A novel guidance framework for nasal rapid antigen tests with improved swab keypoint detectionSmart Health10.1016/j.smhl.2024.10053435(100534)Online publication date: Mar-2025
  • (2022)Translating diagnostics and drug delivery technologies to low-resource settingsScience Translational Medicine10.1126/scitranslmed.abm173214:666Online publication date: 12-Oct-2022

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media