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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1510905
This article is part of the Research Topic Artificial Intelligence-based Multimodal Imaging and Multi-omics in Medical Research View all articles

Comparison of 3D and 2D Area Measurement of Acute Burn Wounds with LiDAR Technique and Deep Learning Model

Provisionally accepted
Che Wei Chang Che Wei Chang 1,2*Hanwei Wang Hanwei Wang 3Feipei Lai Feipei Lai 3Mesakh Christian Mesakh Christian 4Shih Chen Huang Shih Chen Huang 2Han Yi Tsai Han Yi Tsai 2
  • 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 (FEMH), Xinbei, Taiwan
  • 3 Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
  • 4 Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan

The final, formatted version of the article will be published soon.

    It is generally understood that wound areas appear smaller when calculated using 2D images, but the factors contributing to this discrepancy are not well-defined. With the rise of 3D photography, 3D segmentation, and 3D measurement, more accurate assessments have become possible. We developed an application called the Burn Evaluation Network (B.E.N.), which combines a deep learning model with LiDAR technology to perform both 2D and 3D measurements.In the first part of our study, we used burn wound templates to verify that the results of 3D segmentation closely matched the actual size of the burn wound and to examine the effect of limb curvature on the 3D/2D area ratio. Our findings revealed that smaller curvatures, indicative of flatter surfaces, were associated with lower 3D/2D area ratios, and larger curvatures corresponded to higher ratios. For instance, the back had the lowest average curvature (0.027 ± 0.004) and the smallest 3D/2D area ratio (1.005 ± 0.055).In the second part of our study, we applied our app to real patients, measuring burn areas in both 3D and 2D. Regions such as the head and neck (ratio: 1.641) and dorsal foot (ratio: 1.908) exhibited significantly higher 3D/2D area ratios. Additionally, images containing multiple burn wounds also showed a larger ratio (1.656) and greater variability in distribution. These findings suggest that 2D segmentation tends to significantly underestimate surface areas in highly curved regions or when measurements require summing multiple wound areas. We recommend using 3D measurements for wounds located on areas like the head, neck, and dorsal foot, as well as for cases involving multiple wounds or large areas, to improve measurement accuracy.

    Keywords: 2D segmentation, 3D segmentaion, lidar, 3D measurement, Anatomical location, curvature, deep learning - artificial intelligence

    Received: 14 Oct 2024; Accepted: 31 Jan 2025.

    Copyright: © 2025 Chang, Wang, Lai, Christian, Huang and Tsai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Che Wei Chang, Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.