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

(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.

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

(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.

Educational suitability of new channel-type video-laryngoscope with AI-based glottis guidance system for novices wearing personal-protective-equipment.

ABSTRACT: The aim of this study was to determine which of 4 laryngoscopes, including A-LRYNGO, a newly developed channel-type video-laryngoscope with an embedded artificial intelligence-based glottis guidance system, is appropriate for tracheal intubation training in novice medical students wearing personal protective equipment (PPE).Thirty healthy senior medical school student volunteers were recruited. The participants underwent 2 tests with 4 laryngoscopes: Macintosh, McGrath, Pentax Airway-Scope and A-LRYNGO. The first test was conducted just after a lecture without any hands-on workshop. The second test was conducted after a one-on-one hands-on workshop. In each test, we measured the time required for tracheal intubation, intubation success rate, etc, and asked all participants to complete a short questionnaire.The time to completely insert the endotracheal tube with the Macintosh laryngoscope did not change significantly (P = .177), but the remaining outcomes significantly improved after the hands-on workshop (all P < .05). Despite being novice practitioners with no intubation experience and wearing PPE, the, 2 channel-type video-laryngoscopes were associated with good intubation-related performance before the hands-on workshop (all P < .001). A-LRYNGO's artificial intelligence-based glottis guidance system showed 93.1% accuracy, but 20.7% of trials were guided by the vocal folds.To prepare to manage the airway of critically ill patients during the coronavirus disease 2019 pandemic, a channel-type video-laryngoscope is appropriate for tracheal intubation training for novice practitioners wearing PPE.

The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection

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.