Abstract
Burn injuries are a common pediatric health threat with depth assessment relying heavily on subjective visual inspection. While objective techniques like laser Doppler imaging exist, their cost and portability limitations restrict use. We propose SAM-DR to address the challenge of scarce annotated burn data by repurposing pre-trained models with minimal fine-tuning. By replacing SAM’s segmentation head with dense linear regression, our method not only identifies burn locations but also perceives burn depth through continuous depth prediction. Using 294 smartphone images from 94 patients annotated by 9 clinicians, we conducted a pixel-level comparison of human disagreement. SAM-DR achieved a 0.96 Dice score in wound segmentation, establishing state-of-the-art performance, and the use of interactive thresholding enabled segmentation of different burn depths comparable to human experts, suitable for assisted annotation. We developed an interactive tool based on SAM-DR that supports both clinical diagnosis and data annotation, offering a non-contact solution for burn assessment and dataset creation.
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Data availability
The datasets generated and analyzed during the current study are not publicly available due to patient privacy concerns but are available from the corresponding author on reasonable request and with permission from the institutional ethics committee.
References
Lawrence, J. W., Mason, S. T., Schomer, K. & Klein, M. B. Epidemiology and impact of scarring after burn injury: a systematic review of the literature. J. Burn Care Res. 33, 136–146 (2012).
Meng, F. et al. Pediatric burn contractures in low-and lower middle-income countries: A systematic review of causes and factors affecting outcome. Burns 46, 993–1004 (2020).
Phelan, H. A. et al. Use of 816 consecutive burn wound biopsies to inform a histologic algorithm for burn depth categorization. J. Burn Care Res. 42, 1162–1167 (2021).
Shin, J. Y. & Yi, H. S. Diagnostic accuracy of laser doppler imaging in burn depth assessment: Systematic review and meta-analysis. Burns 42, 1369–1376 (2016).
Taib, B. G. et al. Artificial intelligence in the management and treatment of burns: A systematic review and meta-analyses. J. Plastic Reconstruct. Aesthetic Surg. 77, 133–161 (2023).
Zhang, R., Tian, D., Xu, D., Qian, W. & Yao, Y. A survey of wound image analysis using deep learning: classification, detection, and segmentation. IEEE Access 10, 79502–79515 (2022).
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-assisted Intervention, 234–241. (Springer, 2015).
Yadav, D. et al. Spatial attention-based residual network for human burn identification and classification. Sci. Rep. 13, 12516 (2023).
Zhang, X. et al. Multi-feature extraction and selection method to diagnose burn depth from burn images. Electronics 13, 3665 (2024).
Ethier, O. et al. Using computer vision and artificial intelligence to track the healing of severe burns. J. Burn Care Res. 45, 700–708 (2024).
Rahman, M. M. et al. A framework for advancing burn assessment with artificial intelligence. Military Med. 190, 387–393 (2025a).
Rahman, M. M. et al. Ai-driven integrated system for burn depth prediction with electronic medical records: Algorithm development and validation. JMIR Med. Inform. 13, e68366 (2025b).
Bulut, C., Kolca, D. & Tarlak, F. Development a software for detecting burn severity using convolutional neural network-based approach. Sigma 43, 598–606 (2025).
Yıldız, M. et al. Segmentation and classification of skin burn images with artificial intelligence: Development of a mobile application. Burns 50, 966–979 (2024).
Li, Z. et al. Gl-fusionnet: fusing global and local features to classify deep and superficial partial thickness burn. Math. Biosci. Eng. 20, 10153–10173 (2023).
Pabitha, C. & Vanathi, B. Dense mesh rcnn: assessment of human skin burn and burn depth severity. J. Supercomput. 80, 1331–1362 (2024).
Li, X., Liu, Z. & Liu, L. Pediatric burnnet: Robust multi-class segmentation and severity recognition under real-world imaging conditions. SAGE Open Med. 13, 20503121251360090 (2025).
Abdolahnejad, M. et al. Novel cnn-based approach for burn severity assessment and fine-grained boundary segmentation in burn images. IEEE Trans. Instrument. Measure. (2025).
Tan, P., Nyeko-Lacek, M., Walsh, K., Sheikh, Z. & Lewis, C. J. Artificial intelligence-enhanced multispectral imaging for burn wound assessment: Insights from a multi-centre uk evaluation. Burns 107550 (2025).
Kirillov, A. et al. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 4015–4026 (2023).
Ma, J. et al. Segment anything in medical images. Nat. Commun. 15, 654 (2024).
Liu, H., Yue, K., Cheng, S., Li, W. & Fu, Z. A framework for automatic burn image segmentation and burn depth diagnosis using deep learning. Comput. Math. Methods Med. 2021, 5514224 (2021).
Xie, J., Li, H., Li, J. & Xu, X. Ltb-net: A lightweight transformer-based burn depth segmentation network. In BIBE 2024; The 7th International Conference on Biological Information and Biomedical Engineering, 234–239 (2024).
Zhang, D. & Xie, J. Semi-supervised burn depth segmentation network with contrast learning and uncertainty correction. Sensors 25, 1059 (2025).
Chang, C. W. et al. Application of multiple deep learning models for automatic burn wound assessment. Burns 49, 1039–1051 (2023).
Rozo, A. et al. A deep learning image-to-image translation approach for a more accessible estimator of the healing time of burns. IEEE Trans. Biomed. Eng. 70, 2886–2894 (2023).
Ji, S., Xiao, S., Xia, Z., of Burns, C. B. A. T. R. & Trauma Committee, C.-S. M. E. A. o. C. Consensus on the treatment of second-degree burn wounds (2024 edition). Burns Trauma 12, tkad061 (2024).
Wang, W. Advanced auto labeling solution with added features. https://github.com/CVHub520/X-AnyLabeling (2023).
Cubuk, E. D., Zoph, B., Shlens, J. & Le, Q. V. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 702–703 (2020).
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F. & Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European Conference on Computer Vision (ECCV), 801–818 (2018).
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J. & Zisserman, A. The PASCAL visual object classes challenge 2012 (VOC2012) results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html.
Acknowledgements
We thank the clinical staff of Shenzhen Children’s Hospital for their assistance with data collection and coordination. We also acknowledge the technical support provided by the research and engineering team.
Funding
This study was supported by grants from the Guangdong Province Graduate Education Innovation Project (2025JGXM_149).
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H.W. conceived the study, designed the methodology, conducted all experiments, performed the data analysis, and wrote the manuscript. S.Z., W.L., X.C., Q.M., H.C., L.F., Z.Z, T.S. and Z.X. contributed to data collection, clinical assessment, and annotation. S.T. and K.Z. provided technical guidance, supervised model development, and revised the manuscript. K.Z., Y.L., and Z.X. supervised the overall project, provided critical revisions, and approved the final manuscript as corresponding authors. All authors reviewed and approved the final version of the manuscript.
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Wang, H., Zeng, S., Li, W. et al. Interactive AI assisted pediatric burn assessment based on smartphone images. Sci Rep (2026). https://doi.org/10.1038/s41598-026-48169-z
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DOI: https://doi.org/10.1038/s41598-026-48169-z
