@article{ref1,
title="Improving burn depth assessment for pediatric scalds by AI based on semantic segmentation of polarized light photography images",
journal="Burns: journal of the International Society for Burn Injuries",
year="2021",
author="Cirillo, Marco Domenico and Mirdell, Robin and Sjöberg, Folke and Pham, Tuan D.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="This paper illustrates the efficacy of an artificial intelligence (AI) (a convolutional neural network, based on the U-Net), for the burn-depth assessment using semantic segmentation of polarized high-performance light camera images of burn wounds. The proposed method is evaluated for paediatric scald injuries to differentiate four burn wound depths: superficial partial-thickness (healing in 0-7 days), superficial to intermediate partial-thickness (healing in 8-13 days), intermediate to deep partial-thickness (healing in 14-20 days), deep partial-thickness (healing after 21 days) and full-thickness burns, based on observed healing time. In total 100 burn images were acquired. Seventeen images contained all 4 burn depths and were used to train the network. Leave-one-out cross-validation reports were generated and an accuracy and dice coefficient average of almost 97% was then obtained. After that, the remaining 83 burn-wound images were evaluated using the different network during the cross-validation, achieving an accuracy and dice coefficient, both on average 92%. This technique offers an interesting new automated alternative for clinical decision support to assess and localize burn-depths in 2D digital images. Further training and improvement of the underlying algorithm by e.g., more images, seems feasible and thus promising for the future.<p /> <p>Language: en</p>",
language="en",
issn="0305-4179",
doi="10.1016/j.burns.2021.01.011",
url="http://dx.doi.org/10.1016/j.burns.2021.01.011"
}