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15 February 2024 Review of machine learning for optical imaging of burn wound severity assessment
Robert H. Wilson, Rebecca Rowland, Gordon T. Kennedy, Chris Campbell, Victor C. Joe, Theresa L. Chin, David M. Burmeister, Robert J. Christy, Anthony J. Durkin
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Journal of Biomedical Optics, Vol. 29, Issue 2, 020901 (February 2024). https://doi.org/10.1117/1.JBO.29.2.020901
Abstract

Significance

Over the past decade, machine learning (ML) algorithms have rapidly become much more widespread for numerous biomedical applications, including the diagnosis and categorization of disease and injury.

Aim

Here, we seek to characterize the recent growth of ML techniques that use imaging data to classify burn wound severity and report on the accuracies of different approaches.

Approach

To this end, we present a comprehensive literature review of preclinical and clinical studies using ML techniques to classify the severity of burn wounds.

Results

The majority of these reports used digital color photographs as input data to the classification algorithms, but recently there has been an increasing prevalence of the use of ML approaches using input data from more advanced optical imaging modalities (e.g., multispectral and hyperspectral imaging, optical coherence tomography), in addition to multimodal techniques. The classification accuracy of the different methods is reported; it typically ranges from ∼70% to 90% relative to the current gold standard of clinical judgment.

Conclusions

The field would benefit from systematic analysis of the effects of different input data modalities, training/testing sets, and ML classifiers on the reported accuracy. Despite this current limitation, ML-based algorithms show significant promise for assisting in objectively classifying burn wound severity.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Robert H. Wilson, Rebecca Rowland, Gordon T. Kennedy, Chris Campbell, Victor C. Joe, Theresa L. Chin, David M. Burmeister, Robert J. Christy, and Anthony J. Durkin "Review of machine learning for optical imaging of burn wound severity assessment," Journal of Biomedical Optics 29(2), 020901 (15 February 2024). https://doi.org/10.1117/1.JBO.29.2.020901
Received: 30 August 2023; Accepted: 10 January 2024; Published: 15 February 2024
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KEYWORDS
Education and training
Tissues
Image classification
Deep learning
Optical imaging
Machine learning
Skin
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