Abstract
AbstractReducing 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.
Funder
Hallym University Research Fund
Publisher
Springer Science and Business Media LLC
Reference28 articles.
1. Mei, X. et al. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat. Med. 26(8), 1224–1228 (2020).
2. Jin, C. et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat. Commun. 11, 1 (2020).
3. Javor, D. et al. Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography. Eur. J. Radiol. 133, 109402 (2020).
4. Fontanellaz, M. et al. A deep-learning diagnostic support system for the detection of COVID-19 using chest radiographs: A multireader validation study. Investig. Radiol. 56(6), 348–356 (2021).
5. Wang, D., Mo, J., Zhou, G., Xu, L. & Liu, Y. An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PLoS ONE 15, 11 (2020).
Cited by
30 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献