A computational framework to support the treatment of bedsores during COVID-19 diffusion.
Ferdinando Di MartinoFrancesco OrciuoliPublished in: Journal of ambient intelligence and humanized computing (2022)
The treatment of pressure ulcers, also known as bedsores, is a complex process that requires to employ specialized field workforce assisting patients in their houses. In the period of COVID-19 or during any other non-trivial emergency, reaching the patients in their own house is impossible. Therefore, as well as in the other sectors, the adoption of digital technologies is invoked to solve, or at least mitigate, the problem. In particular, during the COVID-19, the social distances should be maintained in order to decrease the risk of contagion. The Project Health Management Systems proposes a complete framework, based on Deep Learning, Augmented Reality. Pattern Matching, Image Segmentation and Edge Detection approaches, to support the treatment of bedsores without increasing the risk of contagion, i.e., improving the remote aiding of specialized operators and physicians and involving inexperienced familiars in the process.
Keyphrases
- deep learning
- end stage renal disease
- public health
- coronavirus disease
- healthcare
- ejection fraction
- newly diagnosed
- sars cov
- palliative care
- primary care
- emergency department
- mental health
- peritoneal dialysis
- convolutional neural network
- machine learning
- social media
- artificial intelligence
- risk assessment
- electronic health record
- health information
- wound healing
- smoking cessation