학술논문

A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns
Document Type
Periodical
Source
IEEE Transactions on Biomedical Engineering IEEE Trans. Biomed. Eng. Biomedical Engineering, IEEE Transactions on. 70(10):2886-2894 Oct, 2023
Subject
Bioengineering
Computing and Processing
Components, Circuits, Devices and Systems
Communication, Networking and Broadcast Technologies
Image color analysis
Imaging
Wounds
Lasers
Doppler effect
Digital images
Skin
Burn care
deep learning
healing time estimation
image-to-image translation
laser doppler imaging
Language
ISSN
0018-9294
1558-2531
Abstract
Objective: An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI. Methods: This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI. Results: Results showed a satisfactory performance in terms of low MAE ($0.2370 \pm 0.0086$). However, the unbalanced distribution of colors in the data affects this performance. Significance: This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas.