Kimura Yuka, Ikuta Kento, Ohga Makoto, Umeda Ryunosuke, Nakagaki Makoto, Suyama Yoshiko, Kanayama Haruka, Konishi Mamoru, Nishikawa Hiroyuki, Yagi Shunjiro
Department of Plastic and Reconstructive Surgery, School of Medicine, Faculty of Medicine, Tottori University, Yonago 683-8504, Japan.
Focus Systems Corporation, Tokyo 141-0022, Japan.
Yonago Acta Med. 2025 Aug 7;68(3):262-268. doi: 10.33160/yam.2025.08.014. eCollection 2025 Aug.
Accurate assessment of pressure injuries is critical in clinical settings, especially when evaluating necrotic tissue using the DESIGN-R® scale widely adopted in Japan. This study aimed to integrate artificial intelligence (AI) into the evaluation process to enhance diagnostic consistency and accuracy. By leveraging deep learning and convolutional neural networks, we explored the potential of AI models in classifying necrotic tissue from wound images.
A retrospective observational study was conducted using electronic medical records and wound photographs from patients treated at Tottori University Hospital between 2014 and 2022. Two supervised learning models were developed: a Categorical Classification Model (CCM) for multi-class prediction, and a Binary Classification Model (BCM) implementing a two-step binary classification. Necrotic tissue was categorized based on the DESIGN-R® scale into three classes: n0 (no necrosis), N3 (soft necrosis), and N6 (hard, adherent necrosis). The models' performance was evaluated using standard classification metrics.
The CCM showed recall rates of 0.7824 for n0, 0.6620 for N3, and 1.0000 for N6. In contrast, the BCM achieved higher recall rates: 0.9074 for n0, 0.9884 for N3, and 1.0000 for N6. Overall metrics for CCM were: accuracy 0.8148, precision 0.8166, and F-1 score 0.8089. The BCM surpassed these with an accuracy of 0.8711, precision 0.8418, and F-1 score 0.8508. Across all performance indicators, the BCM demonstrated superior classification capability.
The study demonstrated that AI, particularly the binary classification approach, can enhance necrotic tissue assessment in pressure injury evaluation. The BCM consistently outperformed the CCM, supporting its potential as a reliable tool to assist clinicians in objective and standardized pressure injury evaluation using the DESIGN-R® framework.
在临床环境中，准确评估压疮至关重要，尤其是在使用日本广泛采用的DESIGN-R®量表评估坏死组织时。本研究旨在将人工智能（AI）整合到评估过程中，以提高诊断的一致性和准确性。通过利用深度学习和卷积神经网络，我们探索了AI模型在从伤口图像中分类坏死组织方面的潜力。
使用鸟取大学医院2014年至2022年期间治疗的患者的电子病历和伤口照片进行了一项回顾性观察研究。开发了两种监督学习模型：用于多类预测的分类模型（CCM）和实施两步二元分类的二元分类模型（BCM）。根据DESIGN-R®量表，坏死组织分为三类：n0（无坏死）、N3（软坏死）和N6（硬的、粘连性坏死）。使用标准分类指标评估模型的性能。
CCM对n0的召回率为0.7824，对N3的召回率为0.6620，对N6的召回率为1.0000。相比之下，BCM实现了更高的召回率：n0为0.9074，N3为0.9884，N6为1.0000。CCM的总体指标为：准确率0.8148，精确率0.8166，F1分数0.8089。BCM在准确率为0.8711、精确率为0.8418和F1分数为0.8508方面超过了这些指标。在所有性能指标中，BCM表现出卓越的分类能力。
该研究表明，人工智能，特别是二元分类方法，可以在压疮评估中增强坏死组织的评估。BCM始终优于CCM，支持其作为一种可靠工具的潜力，以协助临床医生使用DESIGN-R®框架进行客观和标准化的压疮评估。
