Lindborg Karl, Karlsson Matilda, Kotorri Ana, Sjöberg Folke, Fredrikson Mats, Haglind Axel, Sjöberg Zacharias, Elmasry Moustafa
Department of Hand Surgery, Plastic Surgery and Burns, Linköping University Hospital, 581 85 Linkoping, Sweden.
Department of Biomedical and Clinical Sciences, Linköping University, 581 85 Linköping, Sweden.
J Clin Med. 2025 Aug 18;14(16):5838. doi: 10.3390/jcm14165838.
Detailed assessments, documentation, and evaluation of the wound characteristics in hard-to-heal wounds are essential for optimizing and individualizing wound care. However, the remaining challenge in clinical care includes the lack of high accuracy and precision tools for automated wound size (surface area and depth assessment) and a wound bed evaluation, i.e., a qualitative and quantification assessment of slough and necrosis. This study evaluates the accuracy and precision of the AI-powered technique, SeeWound© 2, compared to digital planimetry for a wound surface area and a wound bed characterization (slough and necrosis) in "in vitro" models and in patients, and a probe for depth, including diabetic foot ulcers, venous ulcers, pressure ulcers, and ischemic ulcers. The data show that accuracy and precision (SeeWound© 2) for the wound surface area, the depth, and the wound bed characterization (slough and necrosis) were accuracy 96.28% and 90.00%, (CV 5.56%), respectively (wound size); 90.75% and 89.55%, (CV 3.07%), respectively (wound depth); 80.30% (slough) and 84.73% (necrosis) and 93.51% (slough) (CV 4.15%) and 82.35% (CV 8.34%) (necrosis). The precision for the digital planimetry was 88.61% (CV 7.00%) (slough) 85.74% (CV 7.54%) (necrosis). The overall accuracy and precision of the AI model in identifying wound size and depth were close to 90%, except for the accuracy and precision for slough and necrosis, where levels around 80% were achieved when compared to digital planimetry. The findings for the wound surface area and depth assessments, together with quantification of slough and necrosis, suggest that the SeeWound© 2 model can offer significant clinical benefits by improving documentation and supporting decision-making in wound management.
对难愈合伤口的伤口特征进行详细评估、记录和评价，对于优化伤口护理并使其个性化至关重要。然而，临床护理中仍然存在的挑战包括缺乏用于自动测量伤口大小（表面积和深度评估）以及伤口床评估（即对腐肉和坏死组织进行定性和定量评估）的高精度和高精准度工具。本研究评估了人工智能技术SeeWound© 2与数字平面测量法相比，在“体外”模型和患者中对伤口表面积、伤口床特征（腐肉和坏死组织）以及深度探测（包括糖尿病足溃疡、静脉性溃疡、压疮和缺血性溃疡）的准确性和精准度。数据显示，对于伤口表面积、深度以及伤口床特征（腐肉和坏死组织），SeeWound© 2的准确性分别为96.28%和90.00%（CV 5.56%）（伤口大小）；90.75%和89.55%（CV 3.07%）（伤口深度）；80.30%（腐肉）和84.73%（坏死组织）以及93.51%（腐肉）（CV 4.15%）和82.35%（CV 8.34%）（坏死组织）。数字平面测量法的精准度为88.61%（CV 7.00%）（腐肉），85.74%（CV 7.54%）（坏死组织）。除了腐肉和坏死组织的准确性和精准度外，人工智能模型在识别伤口大小和深度方面的总体准确性和精准度接近90%，与数字平面测量法相比，腐肉和坏死组织的准确性和精准度达到了80%左右。伤口表面积和深度评估的结果，以及腐肉和坏死组织的量化结果表明，SeeWound© 2模型通过改善记录并支持伤口管理中的决策制定，可以提供显著的临床益处。
