ArticleMilitary medicine2023
Autonomous Multi-modality Burn Wound Characterization using Artificial Intelligence.
Article in Military medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Automated Noninvasive Burn Diagnostic System for Health Care Using Artificial Intelligence: AMBUSH-AI.Annals of surgery · 2026Article
- Comparing YOLO and U-net deep learning algorithms in chronic wound image segmentation.BMC medical imaging · 2026Article
- A Framework for Advancing Burn Assessment With Artificial Intelligence.Military medicine · 2025Article
- AI-Driven Integrated System for Burn Depth Prediction With Electronic Medical Records: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Strategies for Optimizing Acute Burn Wound Therapy: A Comprehensive Review.Medicina (Kaunas, Lithuania) · 2025Review
- Current status, challenges, and prospects of artificial intelligence applications in wound repair theranostics.Theranostics · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
introductionBetween 5% and 20% of all combat-related casualties are attributed to burn wounds. A decrease in the mortality rate of burns by about 36% can be achieved with early treatment, but this is contingent upon accurate characterization of the burn. Precise burn injury classification is recognized as a crucial aspect of the medical artificial intelligence (AI) field. An autonomous AI system designed to analyze multiple characteristics of burns using modalities including ultrasound and RGB images is described. MATERIALS AND
methodsA two-part dataset is created for the training and validation of the AI: in vivo B-mode ultrasound scans collected from porcine subjects (10,085 frames), and RGB images manually collected from web sources (338 images). The framework in use leverages an explanation system to corroborate and integrate burn expert's knowledge, suggesting new features and ensuring the validity of the model. Through the utilization of this framework, it is discovered that B-mode ultrasound classifiers can be enhanced by supplying textural features. More specifically, it is confirmed that statistical texture features extracted from ultrasound frames can increase the accuracy of the burn depth classifier.
resultsThe system, with all included features selected using explainable AI, is capable of classifying burn depth with accuracy and F1 average above 80%. Additionally, the segmentation module has been found capable of segmenting with a mean global accuracy greater than 84%, and a mean intersection-over-union score over 0.74.
conclusionsThis work demonstrates the feasibility of accurate and automated burn characterization for AI and indicates that these systems can be improved with additional features when a human expert is combined with explainable AI. This is demonstrated on real data (human for segmentation and porcine for depth classification) and establishes the groundwork for further deep-learning thrusts in the area of burn analysis.
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Registered trials
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