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Application of deep learning to pressure injury staging.
- Source :
- Journal of Wound Care; May2024, Vol. 33 Issue 5, p368-378, 11p
- Publication Year :
- 2024
-
Abstract
- Objective: Accurate assessment of pressure injuries (PIs) is necessary for a good outcome. Junior and non-specialist nurses have less experience with PIs and lack clinical practice, and so have difficulty staging them accurately. In this work, a deep learning-based system for PI staging and tissue classification is proposed to help improve its accuracy and efficiency in clinical practice, and save healthcare costs. Method: A total of 1610 cases of PI and their corresponding photographs were collected from clinical practice, and each sample was accurately staged and the tissues labelled by experts for training a Mask Region-based Convolutional Neural Network (Mask R-CNN, Facebook Artificial Intelligence Research, Meta, US) object detection and instance segmentation network. A recognition system was set up to automatically stage and classify the tissues of the remotely uploaded PI photographs. Results: On a test set of 100 samples, the average precision of this model for stage recognition reached 0.603, which exceeded that of the medical personnel involved in the comparative evaluation, including an enterostomal therapist. Conclusion: In this study, the deep learning–based PI staging system achieved the evaluation performance of a nurse with professional training in wound care. This low-cost system could help overcome the difficulty of identifying PIs by junior and non-specialist nurses, and provide valuable auxiliary clinical information. [ABSTRACT FROM AUTHOR]
- Subjects :
- TISSUE analysis
WORK
STATISTICAL models
WOUND healing
HOSPITAL nursing staff
BANDAGES & bandaging
PHOTOGRAPHY
QUANTITATIVE research
CELL phones
INFORMATION resources
TEACHING methods
DEEP learning
ARTIFICIAL neural networks
TISSUE wounds
DATA analysis software
SURGICAL dressings
WOUND care
QUALITY assurance
PRESSURE ulcers
MEDICAL care costs
EXPERIENTIAL learning
Subjects
Details
- Language :
- English
- ISSN :
- 09690700
- Volume :
- 33
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Journal of Wound Care
- Publication Type :
- Academic Journal
- Accession number :
- 176909602
- Full Text :
- https://doi.org/10.12968/jowc.2024.33.5.368