Real-time remote audio-visual system versus standard buddy system to monitor the doffing of personal protective equipment during COVID-19 pandemic: an observational study

Introduction: Literature states a higher self-contamination rate among healthcare workers (HCWs) while doffing personal protective equipment (PPE). During the COVID-19 pandemic, onsite trained observers were not always available to monitor PPE compliance. The remote audio-visual doffing surveillance (RADS) system has the potential to overcome this limitation [1, 2]. We aimed to compare the efficacy of this real-time RADS system against the onsite buddy system for monitoring the doffing of PPE. Method(s): This prospective, observational study was conducted at our tertiary care centre in northern India. Study was registered in the clinical trial registry India (CTRI/2020/11/038172). 200 HCWs who cared for COVID-19 patients in the intensive care units were included. Group A included HCWs who performed doffing with the help of an onsite trained observer and group B included HCWs who performed doffing with the RADS system. An independent observer noted the error at any step using the CDC doffing checklist, in both groups. An online questionnaire to analyse the level of satisfaction post-doffing was also surveyed. Result(s): The proportion of errors committed during doffing was significantly lower in group B compared to group A with a low relative risk of 0.34 (95% CI 0.22-0.51) (p < 0.001) (Fig. 1). In both groups, there was no difference in HCWs feedback regarding the ease of the system and fear of committing an error. Though the perceived quality of monitoring was felt better with onsite buddy, the overall confidence rating of being safe after doffing was better with the RADS system. Conclusion(s): Real-time RADS system may be more effective than the onsite buddy system for ensuring the safety of HCWs during doffing PPE. HCWs level of satisfaction related to the ease and anxiety with the monitoring systems were comparable. RADS system can reduce reliance on HCW resources and can integrate well into existing healthcare systems.

How to Achieve an Easier, Cost-Effective Hybrid Event Environment: Actual Equipment Cases and its Way to the Future

With the outbreak of COVID-19, the lecture environment at universities has increasingly turned into online environments. In addition to those delivered entirely by online tools, there are hybrid, online and in-person, lecture environments. Hybrid casting environments are growing not only in the classroom, but also in various conferences. In an online-only meeting environment, web conferencing tools such as Zoom and Webex can be used to approximately achieve the objective. In a hybrid environment, however, a face-to-face environment is also necessary, and it is essential to build an environment that is aware of both online and face-to-face interaction. It would be fine if the venue already has the equipment to serve the purpose, but in some cases, there are no facilities and the equipment must be carried in and arranged. At this point, the most difficult point is in the audio system configuration. This requires a certain level of technical knowledge and monetary costs. For a reasonable price, it is possible to outsource to a specialized service provider to create a perfect casting environment. However, in many situations, it is difficult to take significant costs and many people are trying to manage the situations by trial and error. We have experienced various hybrid casting situations. Recently, we try to consider how to reduce costs from various aspects, such as "avoiding high costs in terms of manpower, equipment, and expenses,""not requiring operators to have lots of knowledge,"and "minimizing the amount of equipment to be carried in, as it is integrated with the existing equipment at the venue. In this presentation, we provide actual examples of hybrid casting environments in which the author experienced, mainly by bringing in, setting up, and operating equipment by one person, and outlines the key points of these operations, as well as considering what kind of casting environment can reduce various costs and achieve hybrid casting more easily. We would like to share with the SIGUCCS community what kind of total peripheral environment is needed to make hybrid delivery more familiar, not just the delivery technology itself, such as Zoom or Webex, and to think together about how it should be. © 2023 Owner/Author.

SlowFast GCN Network for Quantification of Parkinsonian Gait Using 2D Videos

Parkinson's disease(PD) is a progressive neu-rodegenerative disease defined by clinical syndrome including bradykinesia, tremor and postural instability. The PD-related disability and impairment are usually monitored by clinicals using the MDS-UPDRS scale. However, due to COVID-19, it became much harder for the patients to reach hospitals and obtain necessary assessment and treatment. Nowadays, 2D videos are easily accessible and can be a promising so-lution for on-site and remote diagnosis of movement disorder. Inspired by the frequency-based video processing mechanism of human visual system, we propose a video-based SlowFast GCN network to quantify the gait disorder. The model consists of two parts: the fast pathway and the slow pathway. The former detects characteristics such as tremor and bilateral asymmetry, while the latter extracts characteristics such as bradykinesia and freezing of gait. Furthermore, in order to investigate the influence of age on the model performance, an aged control group and a young control group were set up for verification. The proposed model was evaluated on a video dataset collected from 68 participants. We achieved a balanced accuracy of 87.5% and precision of 87.9%, which outperformed existing competing methods. When replacing the young healthy controls with the same number of older controls, the balanced accuracy and precision were decreased by 10.4% and 9.7%, which indicates that age has a significant effect on the model perfomance. © 2022 IEEE.

AI-Based Saliency-Aware Video Coding

The demand for video through over-the-top (OTT) has been constantly increasing in recent years. During the COVID-19 pandemic, demand skyrocketed, hence leading to the need for better video compression. The human visual system (HVS) can quickly select visually important regions in its visual field. These regions are captured at high resolution, while other peripheral regions receive little attention. Saliency maps are a way to imitate the HVS attention mechanism. Recently, deep learning-based saliency models have achieved tremendous improvements. This article leverages state-of-the-art deep learning-based saliency models to improve video coding efficiency. First, a saliency-based rate control scheme is integrated in a high-efficiency video encoder (HEVC). Then, a saliency-guided preprocessing filtering step is introduced. Finally, the two approaches are combined. Objective and subjective evaluations show that it can lower the bitrate from 6% to almost 30% while maintaining the same visual quality. © 2002 Society of Motion Picture and Television Engineers, Inc.

A Deep Learning Ensemble Method to Visual Acuity Measurement Using Fundus Images

Visual acuity (VA) is a measure of the ability to distinguish shapes and details of objects at a given distance and is a measure of the spatial resolution of the visual system. Vision is one of the basic health indicators closely related to a person’s quality of life. It is one of the first basic tests done when an eye disease develops. VA is usually measured by using a Snellen chart or E-chart from a specific distance. However, in some cases, such as the unconsciousness of patients or diseases, i.e., dementia, it can be impossible to measure the VA using such traditional chart-based methodologies. This paper provides a machine learning-based VA measurement methodology that determines VA only based on fundus images. In particular, the levels of VA, conventionally divided into 11 levels, are grouped into four classes and three machine learning algorithms, one SVM model and two CNN models, are combined into an ensemble method in order to predict the corresponding VA level from a fundus image. Based on a performance evaluation conducted using randomly selected 4000 fundus images, we confirm that our ensemble method can estimate with 82.4% of the average accuracy for four classes of VA levels, in which each class of Class 1 to Class 4 identifies the level of VA with 88.5%, 58.8%, 88%, and 94.3%, respectively. To the best of our knowledge, this is the first paper on VA measurements based on fundus images using deep machine learning.

GAS LEAKS FROM BIOPSY VALVES OF GASTROINTESTINAL ENDOSCOPY-ITS VISUALIZATION AND SEMI=QUANTIFICATION UTILIZING SCHLIEREN OPTICAL SYSTEM

<Background> With the global epidemic of COVID-19, there has been a growing concern about the risk of exposure to the virus among healthcare workers. Gastrointestinal (GI) endoscopy has been considered as one of the high infectious procedures because of the high risk of aerosol exposure. However, that caution is mainly directed at secretions and aerosols from the patient's mouth, and less attention is currently paid to air leaks from the endoscopic system itself. Although a few reports have been published on air leaks from GI endoscopic systems, no systematic and quantitative studies of air leaks have been conducted. Schlieren system is an optical device for visualizing minute changes in airflow that are invisible to the naked eye, by using differences in the refractive index of the medium, and has been mainly used in the field of engineering. We aimed to systematically evaluate air leaks from GI endoscopic systems using Schlieren system, and to determine the relationship between the amount of leakage and insufflation conditions including the types of biopsy valves. <Methods> The following experiments were performed on explanted swine stomachs while maintaining an intra-gastric pressure at each preset value. We attempted to visualize air leaks using System Schlieren (SS100, Kato Koken, Kanagawa, Japan). In all experiments, biopsy forceps were inserted and withdrawn 3 cm per 3 seconds. This action was repeated five times in each experiment. Experiment 1: Examined the feasibility of the Schlieren device in visualizing air leaks from biopsy valves. Experiment 2: The intragastric pressure was varied in the range of 4 to 15 mmHg and the air leaks were quantified and compared in each pressure. Experiment 2: Compared the air leaks between the types of biopsy valves, e.g. reusable, disposable, universal, deteriorated reusable valves after more than 10 times of uses. Image J (National Institute of Health, US) was used to measure the initial velocity and area of the leak on obtained images. The average values of initial velocity and leakage area over five sessions were calculated. For the leak area analysis, image analysis was performed for both forceps insertion and withdrawal. <Results> Experiment 1: Air leaks were seen at the moment of forceps insertion and withdrawal (Fig 1). Experiment 2: There was a linear relationship between intragastric pressure and initial velocity/diffusion range of the leak (Fig2A). Experiment 3: Disposable and deteriorated reusable biopsy valves had larger initial velocity/diffusion range of the leak (Fig2B). <Conclusions> We successfully visualized air leaks from GI endoscopic systems using the Schlieren system. We herein recommend the use of lower intragastric pressure at the times of insertion/withdrawal of forceps, and avoidance of using disposable/deteriorated biopsy valves, for less gas leakage and possibly less aerosol exposure.(Figure presented)Figure1. Visualization of air leak from biopsy valve(Figure presented)Figure 2. Results of analysis on air leakage from biopsy valve

Comparison of remote and in-person tutorials of color appearance phenomena

Accurately describing the effect of lighting on color appearance phenomena is critical for color science education. While it is ideal to conduct in-person tutorials to demonstrate the color appearance fundamentals, laboratory tutorials have been limited due to COVID-19. The limitation of in-person gatherings and the increase popularity of remote teaching help evoke alternative methods to demonstrate color appearance phenomena. Here, a remote tutorial method is described, and results are compared to in-person tutorials. While the remote tutorial had weaker result in representing observers’ color experience compared to the in-person lab tutorial, remote demonstrations can be used to demonstrate and discuss the limitations of color imaging, and the difference between the human visual system and digital imaging systems. © 2021 Society for Imaging Science and Technology. All rights reserved.

[Ophthalmological manifestations of post-COVID-19 syndrome]. / Oftal'mologicheskie proyavleniya postkovidnogo sindroma.

The review presents data on damage to the organ of vision in patients recovered from the new coronavirus infection reflecting on the results obtained by various researchers from examining patients with varying severity of the disease, both during the active stage and after recovery. Possible ways of transmission of the infectious agent into the organ of vision were analyzed. The most common ocular manifestation of COVID-19 is conjunctivitis, while lesions of the retina and optic nerve were noted less often. The article also outlines the strategy for treatment and describes infection prevention measures for doctors and patients.

Visual Algorithm of VR E-Sports for Online Health Care.

The need for non-face-to-face online health care has emerged through the era of "untact". However, there is a lack of standardization work and research cases on the exercise effect of immersive content. In this study, the possibility of the exercise effect of VR e-sports among e-sports cases were presented through a visual algorithm analysis. In addition, the evaluation criteria were established. The research method compares and analyzes e-sports cases and VR e-sports cases by applying existing evaluation research cases. It also sets up a new evaluation standard. As for the analysis result, the device immersion method and interaction range were set through an algorithm analysis; FOV and frame immersion were set through typification; the user recognition method and interaction method were set through the visual diagram. Then, each derived result value was quantified and a new evaluation criterion was proposed.