AI-Based Wound Measurement in a Telemedicine-Style Workflow: Agreement With In-Person Reference Measurements Compared With Individual Physicians and Expert Consensus.

Accurate wound size measurement is essential for monitoring healing, yet visual estimates are variable-especially in telemedicine-style assessments. To compare AI-based wound size estimates with physician image-based estimates using in-person measurements as ground truth (GT). In a prospective dataset of bedside wound photographs (Dec 2024-Apr 2025), GT height and width were measured in person by two experienced raters using a calibrated ruler. A set of ArUco markers placed adjacent to the wound at the same skin level enabled image-based retrieval of real-world size information. Five physicians estimated height and width from images in a telemedicine-style workflow. Two deep-learning segmentation architectures (TransNeXt-Tiny [TransNeXt] and VWFormerConvNeXtS [VW-Conv]) generated automated size estimates. Agreement and error were quantified using ICC(2,1), Pearson's r, MAE, bias and Bland-Altman limits of agreement. In addition, pixel-level segmentation performance of both models was quantified against expert-annotated reference masks using Intersection-over-Union (IoU), the Dice coefficient, precision and recall, reported as macro-averaged (per-image) and micro-averaged (pixel-pooled) values. AI achieved excellent agreement for height (TransNeXt ICC = 0.96, MAE = 0.82 cm; VW-Conv ICC = 0.95, MAE = 0.68 cm) with minimal bias. For width, AI showed moderate-to-strong agreement with a tendency to underestimation (TransNeXt ICC = 0.86, MAE = 0.69 cm, bias = -0.42 cm; VW-Conv ICC = 0.77, MAE = 0.74 cm, bias = -0.64 cm). Individual physician performance was heterogeneous, while a human consensus improved agreement (height ICC = 0.94, MAE = 0.82 cm; width ICC = 0.91, MAE = 0.65 cm). At the pixel level, TransNeXt reached a macro-averaged Dice of 0.886 (IoU 0.805) and VW-Conv a macro-averaged Dice of 0.818 (IoU 0.733), with TransNeXt showing the more consistent per-image performance. AI-based wound measurement was more consistent than single physician estimates and reached agreement comparable to, but not exceeding, a consensus of experts. Within the constraints of this single-centre validation study, these findings support AI-assisted measurement as an objective aid for standardised wound monitoring in telemedicine and clinical research; confirmation in larger, multicentre and prospectively recruited cohorts is required.