TY  - JOUR
PY  - 2021//
TI  - Improving burn depth assessment for pediatric scalds by AI based on semantic segmentation of polarized light photography images
JO  - Burns: journal of the International Society for Burn Injuries
A1  - Cirillo, Marco Domenico
A1  - Mirdell, Robin
A1  - Sjöberg, Folke
A1  - Pham, Tuan D.
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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>
LA  - en
SN  - 0305-4179
UR  - http://dx.doi.org/10.1016/j.burns.2021.01.011
ID  - ref1
ER  -