ArticleIEEE open journal of engineering in medicine and biology2021
Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention.
Article in IEEE open journal of engineering in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled it
- Computer vision applications in vascular surgery: a systematic review and critical appraisal.NPJ digital medicine · 2026Article
- Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet.IEEE open journal of engineering in medicine and biology · 2024Article
- Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models.IEEE open journal of engineering in medicine and biology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
goalChronic wounds affect 6.5 million Americans. Wound assessment via algorithmic analysis of smartphone images has emerged as a viable option for remote assessment.
methodsWe 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.
resultsIn 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%.
conclusionsOur 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.
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