Department of Radiology
Analytical, Diagnostic and Therapeutic Techniques and Equipment | Artificial Intelligence and Robotics | Bioimaging and Biomedical Optics | Biomedical Devices and Instrumentation | Health Information Technology | Radiology
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.
Chronic wounds, deep learning, medical imaging, smartphone assessment, transfer learning
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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/
DOI of Published Version
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
IEEE open journal of engineering in medicine and biology
Liu Z, Agu E, Pedersen P, Lindsay C, Tulu B, Strong D. (2021). Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention. Radiology Publications. https://doi.org/10.1109/ojemb.2021.3092207. Retrieved from https://escholarship.umassmed.edu/radiology_pubs/653
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Artificial Intelligence and Robotics Commons, Bioimaging and Biomedical Optics Commons, Biomedical Devices and Instrumentation Commons, Health Information Technology Commons, Radiology Commons