Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Augmented Sensitivity of At-Home Rapid SARS-CoV-2 Antigen Test (RAT) Kits with Computer Vision

Version 1 : Received: 27 February 2022 / Approved: 2 March 2022 / Online: 2 March 2022 (08:06:49 CET)

A peer-reviewed article of this Preprint also exists.

Miikki, K.; Miikki, L.; Wiklund, J.; Karakoç, A. Augmented Sensitivity of At-Home Rapid SARS-CoV-2 Antigen Test (RAT) Kits with Computer Vision: A Framework and Proof of Concept. BioMed 2022, 2, 199-209. Miikki, K.; Miikki, L.; Wiklund, J.; Karakoç, A. Augmented Sensitivity of At-Home Rapid SARS-CoV-2 Antigen Test (RAT) Kits with Computer Vision: A Framework and Proof of Concept. BioMed 2022, 2, 199-209.

Journal reference: BioMed 2022, 2, 18
DOI: 10.3390/biomed2020018

Abstract

At-home rapid antigen test (RAT) kits for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are valuable public health tools during the present coronavirus disease (COVID-19) pandemic. They provide fast identification of coronavirus infection, which can help to reduce the transmission rates and burden on the healthcare system. However, they have lower sensitivity when compared with the reverse transcription polymerase chain reaction (RT-PCR) tests. One of the reasons for the lower sensitivity is due to the RAT color indicators being indistinct or invisible to the naked eye after the measurements. For this reason, we propose a systematic approach, through which we investigated anonymously provided at-home RAT kit results by using our in-house open source image processing scripts developed for affordable Raspberry Pi computer and Raspberry Pi HQ camera systems (available at https://github.com/kmiikki/ratcv). Therefore, we aimed at minimizing the human-related analysis errors for such kits. We believe that our framework can contribute to reduced the delayed quarantines of infected individuals and spreading of the current infectious disease.

Keywords

SARS-CoV-2; rapid antigen test; RT-PCR test; COVID-19; image processing; Raspberry Pi

Subject

LIFE SCIENCES, Virology

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