Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention
UMass Chan Affiliations
RadiologyDocument Type
Journal ArticlePublication Date
2021-06-24Keywords
Chronic woundsdeep learning
medical imaging
smartphone assessment
transfer learning
Analytical, Diagnostic and Therapeutic Techniques and Equipment
Artificial Intelligence and Robotics
Bioimaging and Biomedical Optics
Biomedical Devices and Instrumentation
Health Information Technology
Radiology
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Goal: Chronic wounds affect 6.5 million Americans. Wound assessment via algorithmic analysis of smartphone images has emerged as a viable option for remote assessment. Methods: We comprehensively score wounds based on the clinically-validated Photographic Wound Assessment Tool (PWAT), which comprehensively assesses clinically important ranges of eight wound attributes: Size, Depth, Necrotic Tissue Type, Necrotic Tissue Amount, Granulation Tissue type, Granulation Tissue Amount, Edges, Periulcer Skin Viability. We proposed a DenseNet Convolutional Neural Network (CNN) framework with patch-based context-preserving attention to assess the 8 PWAT attributes of four wound types: diabetic ulcers, pressure ulcers, vascular ulcers and surgical wounds. Results: In an evaluation on our dataset of 1639 wound images, our model estimated all 8 PWAT sub-scores with classification accuracies and F1 scores of over 80%. Conclusions: Our work is the first intelligent system that autonomously grades wounds comprehensively based on criteria in the PWAT rubric, alleviating the significant burden that manual wound grading imposes on wound care nurses.Source
Liu Z, Agu E, Pedersen P, Lindsay C, Tulu B, Strong D. Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention. IEEE Open J Eng Med Biol. 2021;2:224-234. doi: 10.1109/ojemb.2021.3092207. Epub 2021 Jun 24. PMID: 34532712; PMCID: PMC8442961. Link to article on publisher's site
DOI
10.1109/ojemb.2021.3092207Permanent Link to this Item
http://hdl.handle.net/20.500.14038/48555PubMed ID
34532712Related Resources
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information see https://creativecommons.org/licenses/by-nc-nd/4.0/Distribution License
http://creativecommons.org/licenses/by-nc-nd/4.0/ae974a485f413a2113503eed53cd6c53
10.1109/ojemb.2021.3092207
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