Abstract Assessment of burn extent and depth are critical and require very specialized diagnosis.Automated image-based algorithms could assist in Handriers 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 hair 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.