Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT–PCR Testing

Autor: Yoonje Lee, Yu-Seop Kim, Da In Lee, Seri Jeong, Gu Hyun Kang, Yong Soo Jang, Wonhee Kim, Hyun Young Choi, Jae Guk Kim
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Viruses, Vol 15, Iss 2, p 304 (2023)
Druh dokumentu: article
ISSN: 15020304
1999-4915
DOI: 10.3390/v15020304
Popis: (1) Background: Rapid and accurate negative discrimination enables efficient management of scarce isolated bed resources and adequate patient accommodation in the majority of areas experiencing an explosion of confirmed cases due to Omicron mutations. Until now, methods for artificial intelligence or deep learning to replace time-consuming RT-PCR have relied on CXR, chest CT, blood test results, or clinical information. (2) Methods: We proposed and compared five different types of deep learning algorithms (RNN, LSTM, Bi-LSTM, GRU, and transformer) for reducing the time required for RT-PCR diagnosis by learning the change in fluorescence value derived over time during the RT-PCR process. (3) Results: Among the five deep learning algorithms capable of training time series data, Bi-LSTM and GRU were shown to be able to decrease the time required for RT–PCR diagnosis by half or by 25% without significantly impairing the diagnostic performance of the COVID-19 RT–PCR test. (4) Conclusions: The diagnostic performance of the model developed in this study when 40 cycles of RT–PCR are used for diagnosis shows the possibility of nearly halving the time required for RT–PCR diagnosis.
Databáze: Directory of Open Access Journals
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