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. Similar content being viewed by others 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