ArticleScientific reports2023
Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence in Burn and Wound Care: Image Analysis, Prediction, and Clinical Integration.Journal of burn care & research : official publication of the American Burn Association · 2026Review
- Burn Extent and Fitzpatrick Skin Tone Assessment from Clinical Photographs: Systematic and Random Error in Multimodal Large Language Models.Bioengineering (Basel, Switzerland) · 2026Article
- Use of Artificial Intelligence in Burn Assessment: A Scoping Review with a Large Language Model-Generated Decision Tree.European burn journal · 2026Review
- Article
- Review
- A Framework for Advancing Burn Assessment With Artificial Intelligence.Military medicine · 2025Article
- Top-k Bottom All butDiagnostics (Basel, Switzerland) · 2025Article
- AI-Driven Integrated System for Burn Depth Prediction With Electronic Medical Records: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Intention to Use Automated Diagnosis and Clinical Risk Perceptions Among First Contact Clinicians in Resource-Poor Settings: Questionnaire-Based Study Focusing on Acute Burns.JMIR human factors · 2025Article
- Mobile applications for the assessment of paediatric burn injuries in the Pacific Islands: A Samoan perspective for global research engagement.Journal of public health research · 2025Article
- Review of machine learning for optical imaging of burn wound severity assessment.Journal of biomedical optics · 2024Review
- Spatial attention-based residual network for human burn identification and classification.Scientific reports · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated image-based algorithms could assist in performing wound detection and classification. We aimed to develop two deep-learning algorithms that respectively identify burns, and classify whether they require surgery. An additional aim assessed the performances in different Fitzpatrick skin types. Annotated burn (n = 1105) and background (n = 536) images were collected. Using a commercially available platform for deep learning algorithms, two models were trained and validated on 70% of the images and tested on the remaining 30%. Accuracy was measured for each image using the percentage of wound area correctly identified and F1 scores for the wound identifier; and area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity for the wound classifier. The wound identifier algorithm detected an average of 87.2% of the wound areas accurately in the test set. For the wound classifier algorithm, the AUC was 0.885. The wound identifier algorithm was more accurate in patients with darker skin types; the wound classifier was more accurate in patients with lighter skin types. To conclude, image-based algorithms can support the assessment of acute burns with relatively good accuracy although larger and different datasets are needed.
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