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Mitigation effect of face shield to reduce SARS-CoV-2 airborne transmission risk: Preliminary simulations based on computed tomography.

ABSTRACT: We aimed to develop a model to quantitatively assess the potential effectiveness of face shield (visor) in reducing airborne transmission risk of the novel coronavirus SARS-CoV-2 during the current COVID-19 pandemic using the computational fluid dynamics (CFD) method. The studies with and without face shield in both an infected and healthy person have been considered in indoor environment simulation. In addition to the influence of the face shield and the synchronization of the breathing process while using the device, we also simulated the effect of small air movements on the SARS-CoV-2 infection rate (outdoor environment simulation). The contact with infectious particles in the case without a face shield was 12-20 s (s), in the presence of at least one person who was positive for SARS-CoV-2. If the infected person wore a face shield, no contact with contaminated air was observed during the entire simulation time (80 s). The time of contact with contaminated air (infection time) decreases to about 11 s when the surrounding air is still and begins to move at a low speed. Qualitative differences between simulations performed on the patients with and without the face shield are clearly visible. The maximum prevention of contagion is probably a consequence of wearing a face shield by an infected person. Our results suggest that it is possible to determine contact with air contaminated by SARS-CoV-2 using the CFD method under realistic conditions for virtually any situation and configuration. The proposed method is probably the fastest and most reliable among those based on CFD-based techniques.

SUBMITTER: Tretiakow D

PROVIDER: S-EPMC8081583 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AimTo compare deep learning and experienced physicians in diagnosing gangrenous cholecystitis using computed tomography images and explore the feasibility of diagnostic assistance for acute cholecystitis requiring emergency surgery.MethodsThis retrospective study included 25 patients with pathologically confirmed gangrenous cholecystitis and 129 patients with noncomplicated acute cholecystitis who underwent computed tomography between 2016 and 2021 at two institutions. All available computed tomography images at the time of the initial diagnosis were used for the analysis. A deep learning model based on a convolutional neural network was trained using 1,517 images of 112 patients (18 patients with gangrenous cholecystitis and 94 patients with acute cholecystitis) and tested with 68 images of 42 patients (seven patients with gangrenous cholecystitis and 35 patients with acute cholecystitis). Three blinded, experienced physicians independently interpreted the test images. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were compared between the convolutional neural network and the reviewers.ResultsThe convolutional neural network (sensitivity, 0.70; 95% confidence interval [CI], 0.44-0.87, specificity, 0.93; 95% CI, 0.88-0.96, accuracy, 0.89; 95% CI, 0.81-0.95, area under the receiver operating characteristic curve, 0.84; 95% CI, 0.68-1.00) had achieved a better diagnostic performance than the reviewers (ex. sensitivity, 0.55; 95% CI, 0.30-0.77, specificity, 0.67; 95% CI, 0.62-0.71, accuracy, 0.65; 95% CI, 0.57-0.72, area under the receiver operating characteristic curve, 0.63; 95% CI, 0.44-0.82; P = 0.048 for area under the receiver operating characteristic curve versus convolutional neural network).ConclusionsDeep learning had a better diagnostic performance than experienced reviewers in diagnosing gangrenous cholecystitis and has potential applicability for assisting in identifying indications for emergency surgery in the future.

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Mitigation effect of face shield to reduce SARS-CoV-2 airborne transmission risk: Preliminary simulations based on computed tomography.

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