Deep Learning-Assisted Burn Wound Diagnosis: Diagnostic Model Development Study

Che Wei Chang; Feipei Lai; Mesakh Christian; Yu Chun Chen; Ching Hsu; Yo Shen Chen; Dun Hao Chang; Tyng Luen Roan; Yen Che Yu

doi:10.2196/22798

Deep Learning-Assisted Burn Wound Diagnosis: Diagnostic Model Development Study

JMIR Med Inform. 2021 Dec 2;9(12):e22798. doi: 10.2196/22798.

Authors

Che Wei Chang^{1

2}, Feipei Lai¹, Mesakh Christian³, Yu Chun Chen³, Ching Hsu¹, Yo Shen Chen², Dun Hao Chang^{2

4}, Tyng Luen Roan², Yen Che Yu²

Affiliations

¹ Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan.
² Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
³ Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan.
⁴ Department of Information Management, Yuan Ze University, Chung-Li, Taiwan.

PMID: 34860674
PMCID: PMC8686480
DOI: 10.2196/22798

Abstract

Background: Accurate assessment of the percentage total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancies in estimating %TBSA by different doctors.

Objective: We propose a method, based on deep learning, for burn wound detection, segmentation, and calculation of %TBSA on a pixel-to-pixel basis.

Methods: A 2-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected from medical records and labeled by burn surgeons, and the data set was then input into 2 deep learning architectures, U-Net and Mask R-CNN, each configured with 2 different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another data set, which was also input into U-Net and Mask R-CNN to segment the hands. The %TBSA of burn wounds was then calculated by comparing the pixels of mask areas on images of the burn wound and hand of the same patient according to the rule of hand, which states that one's hand accounts for 0.8% of TBSA.

Results: A total of 2591 images of burn wounds were collected and labeled to form the burn wound data set. The data set was randomly split into training, validation, and testing sets in a ratio of 8:1:1. Four hundred images of volar hands were collected and labeled to form the hand data set, which was also split into 3 sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with DC values of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons.

Conclusions: This is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately, thereby helping to provide precise care to burn victims.

Keywords: burn wounds; deep learning; instance segmentation; percentage total body surface area; semantic segmentation.

©Che Wei Chang, Feipei Lai, Mesakh Christian, Yu Chun Chen, Ching Hsu, Yo Shen Chen, Dun Hao Chang, Tyng Luen Roan, Yen Che Yu. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 02.12.2021.