A COVID-19 Airway Management Innovation with Pragmatic Efficacy Evaluation: The Patient Particle Containment Chamber

Maloney, Lauren; Yang, Ariel; Princi, Rudolph A; Eichert, Alexander J; Hebert, D; Kupec, Taelyn V; Mertz, Alexander E; Vasyltsiv, Roman; Kumar, Thea M Vijaya; Walker, Griffin J; Peralta, Edder J; Hoffman, J.; Page, Christopher R.

doi:10.1007/s10439-020-02599-6

Cited by 11 publications

(8 citation statements)

References 24 publications

(35 reference statements)

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“…In Ref. [ 18 ], the authors have described a new particle containment chamber for patients. The essential criteria of this chamber are: construction from inexpensive materials readily available, reduction in aerosol transmission of at least 90% measured by pragmatic tests, easy to clean and compatibility with common EMS stretchers.…”

Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models

et al. 2022

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Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a major health crisis in the world. However, the early detection of this anomaly is a key task to minimize their spread. Artificial intelligence is one of the approaches commonly used by researchers to discover the problems it causes and provide solutions. These estimates would help enable health systems to take the necessary steps to diagnose and track cases of COVID. In this review, we intend to offer a novel method of automatic detection of COVID-19 using tomographic images (CT) and radiographic images (Chest X-ray). In order to improve the performance of the detection system for this outbreak, we used two deep learning models: the VGG and ResNet. The results of the experiments show that our proposed models achieved the best accuracy of 99.35 and 96.77% respectively for VGG19 and ResNet50 with all the chest X-ray images.

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Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models

et al. 2022

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“…For instance, in Ref. 15 , the study proposes a new design for a device called the patient particle containment chamber (PPCC) for COVID-19 airways management, together with a pragmatic evaluation of its efficiency. The following critical design criteria for the PPCC device are considered: reduction of aerosol transmission by at least 90% as measured by pragmatic testing; construction from readily available, inexpensive materials; easy to clean; and compatibility with common EMS stretcher.…”

Section: Introductionmentioning

confidence: 99%

A Fully-Automated Method to Evaluate Coronavirus Disease Progression with COVID-19 Cough Sounds Using Minimal Phase Information

Sattar

2021

Ann Biomed Eng

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This paper focuses on an important issue of disease progression of COVID-19 (coronavirus disease 2019) through processing COVID-19 cough sounds by proposing a fully-automated method. The new method is based on time-domain exploiting only phase 1 data which is always available for any cough events. The proposed approach generates plausible click sequences consist of clicks for various cough samples from covid-19 patients. The click sequence, which is extracted from the phase slope function of an input signal, is used to calculate inter-click intervals (ICIs), and thereby a scoring index (SI) is derived based on coefficient of variation(CV) of the extracted ICIs. Moreover, probability density function (pdf) of the output click sequence is obtained. The method does not need to adjust any parameters. The experimental results achieved from real-recorded COVID-19 cough data using the medically annotated Novel Coronavirus Cough Database (NoCoCoDa) reveal that the proposed time-domain method can be a very useful tool for automatic cough sound processing to determine the disease progression of coronavirus patients.

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“…18 , 19 , 22 , 25 Beyond 3D printing, collaborative efforts were made to manufacture face cloth coverings 11 and methods to decrease contact with patients during airway management, such as particle containment chambers. 17 …”

Section: Introductionmentioning

confidence: 99%

3D Printed N-95 Masks During the COVID-19 Pandemic: Lessons Learned

et al. 2021

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Early in 2020, the pandemic resulted in an enormous demand for personal protective equipment (PPE), which consists of face masks, face shields, respirators, and gowns. At our institution, at the request of hospital administration, the Lifespan 3D Printing Laboratory spearheaded an initiative to produce reusable N95 masks for use in the hospital setting. Through this article, we seek to detail our experience designing and 3D printing an N95 mask, highlighting the most important lessons learned throughout the process. Foremost among these, we were successful in producing a non-commercial N95 alternative mask which could be used in an era when N95 materials were extremely limited in supply. We identified five key lessons related to design software, 3D printed material airtightness, breathability and humidity dispersal, and ability for communication. By sharing our experience and the most valuable lessons we learned through this process, we hope to provide a helpful foundation for future 3D-printed N95 endeavors.

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A COVID-19 Airway Management Innovation with Pragmatic Efficacy Evaluation: The Patient Particle Containment Chamber

Cited by 11 publications

References 24 publications

Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models

Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models

A Fully-Automated Method to Evaluate Coronavirus Disease Progression with COVID-19 Cough Sounds Using Minimal Phase Information

3D Printed N-95 Masks During the COVID-19 Pandemic: Lessons Learned

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