The Application of a Deep Learning System Developed to Reduce the Time for RT-PCR in COVID-19 Detection

Overview

Journal Sci Rep

Specialty Science

Date 2022 Jan 25

PMID 35075153

Authors

Yoonje Lee

Yu-Seop Kim

Da-In Lee

Seri Jeong

Gu-Hyun Kang

Yong Soo Jang

Wonhee Kim

Hyun Young Choi

Jae Guk Kim

Sang-Hoon Choi

Affiliations

Soon will be listed here.

Abstract

Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction (RT-PCR), which requires 40 cycles to diagnose COVID-19. We propose a deep learning (DL) model to improve the speed of COVID-19 RT-PCR diagnosis. We developed and tested a DL model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 RT-PCR tests. Among the DL models developed here, the diagnostic performance of the 21st model showed an area under the receiver operating characteristic (AUROC), sensitivity, and specificity of 84.55%, 93.33%, and 75.72%, respectively. The diagnostic performance of the 24th model showed an AUROC, sensitivity, and specificity of 91.27%, 90.00%, and 92.54%, respectively.

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