Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care.

Deep Learning has a large impact on medical image analysis and lately has been adopted for clinical use at the point of care. However, there is only a small number of reports of long-term studies that show the performance of deep neural networks (DNNs) in such an environment. In this study, we measured the long-term performance of a clinically optimized DNN for laryngeal glottis segmentation. We have collected the video footage for two years from an AI-powered laryngeal high-speed videoendoscopy imaging system and found that the footage image quality is stable across time. Next, we determined the DNN segmentation performance on lossy and lossless compressed data revealing that only 9% of recordings contain segmentation artifacts. We found that lossy and lossless compression is on par for glottis segmentation, however, lossless compression provides significantly superior image quality. Lastly, we employed continual learning strategies to continuously incorporate new data into the DNN to remove the aforementioned segmentation artifacts. With modest manual intervention, we were able to largely alleviate these segmentation artifacts by up to 81%. We believe that our suggested deep learning-enhanced laryngeal imaging platform consistently provides clinically sound results, and together with our proposed continual learning scheme will have a long-lasting impact on the future of laryngeal imaging.

Effects of surgical masks on aerosol dispersion in professional singing.

BACKGROUND: In the CoVID-19 pandemic, singing came into focus as a high-risk activity for the infection with airborne viruses and was therefore forbidden by many governmental administrations. OBJECTIVE: The aim of this study is to investigate the effectiveness of surgical masks regarding the spatial and temporal dispersion of aerosol and droplets during professional singing. METHODS: Ten professional singers performed a passage of the Ludwig van Beethoven's "Ode of Joy" in two experimental setups-each with and without surgical masks. First, they sang with previously inhaled vapor of e-cigarettes. The emitted cloud was recorded by three cameras to measure its dispersion dynamics. Secondly, the naturally expelled larger droplets were illuminated by a laser light sheet and recorded by a high-speed camera. RESULTS: The exhaled vapor aerosols were decelerated and deflected by the mask and stayed in the singer's near-field around and above their heads. In contrast, without mask, the aerosols spread widely reaching distances up to 1.3 m. The larger droplets were reduced by up to 86% with a surgical mask worn. SIGNIFICANCE: The study shows that surgical masks display an effective tool to reduce the range of aerosol dispersion during singing. In combination with an appropriate aeration strategy for aerosol removal, choir singers could be positioned in a more compact assembly without contaminating neighboring singers all singers.