In early 2020, before a COVID-19 vaccine and effective treatments were widely available, universal mask-wearing was a central strategy to prevent transmission of COVID-19. However, hospitals and other facilities where masks are mandatory have faced challenges. Reminding patients, visitors, and employees to wear masks had to be done manually, which was time-consuming and labor-intensive.
Researchers at the Massachusetts Institute of Technology (MIT) and the Massachusetts Institute of Technology (MIT) set out to test a tool that uses computer vision algorithms to automate mask-wearing monitoring and reminders.
The team conducted a pilot study with volunteered hospital employees and found the technology to work effectively, with most participants reporting positive experiences operating the system at hospital entrances. I discovered that The results of this study are BMJ Open.
“Moving behaviors such as mask wearing requires a lot of effort, even among health professionals,” said lead author Peter Chai, M.D., Ph.D., Emergency Medicine Department, MMS.
“Our research suggests that such a computer visualization system could be useful in the next respiratory virus pandemic. Masks could help control the spread of infection.” It is an essential strategy in the hospital environment to
“We are aware of the challenges in ensuring proper mask use and the potential barriers associated with HR-based notification of mask misuse by colleagues. I will discuss the proposal and my colleagues’ evaluation of the platform’s initial acceptability,” said the senior author. He is C. Giovanni Traverso, MB, BChir, Ph.D., from BWH’s School of Medicine and MIT’s School of Mechanical Engineering.
For this study, the team used a computer vision program developed using low-resolution closed-circuit television still frames to detect mask wearing. Between April 26, 2020 and April 30, 2020, researchers will participate in an observational study testing computer vision models of employees trying to enter one of the hospital’s main entrances. I invited you to The team enrolled his 111 participants who operated the system and surveyed them about their experiences.
The computer visualization system accurately detected mask adhesion 100% of the time. Most participants (87%) reported having a positive experience operating the system at the hospital.
The pilot was limited to one hospital employee and may not be generalizable to other settings. Additionally, behaviors and attitudes toward masking have changed over the course of the pandemic and may vary across the United States. Further research is needed to identify the barriers.
“Our data can help individuals in hospital settings detect and remind them of effective mask wearing as a way to keep themselves safe while working on the front lines, especially in the midst of a pandemic. It suggests that we are open to the use of computer visualization systems to help provide a medical emergency,” Chai said.
“Continued development of detection systems may provide useful tools in the context of the COVID-19 pandemic or as a preparedness to prevent future spread of airborne pathogens.”
Peter R Chai et al. Acceptance of a Computer Vision Facilitated Protocol to Measure Adherence to Face Mask Use: A Single Center Observational Cohort Study Among Hospital Staff. BMJ Open (2022). DOI: 10.1136/bmjopen-2022-062707
Courtesy of Brigham and Women’s Hospital
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