Misir Abdulhamit
Department of Orthopedics and Traumatology, Bahçeşehir University Faculty of Medicine, Medicalpark Goztepe Hospital, Istanbul, Turkey.
J Diabetes Sci Technol. 2025 Aug 3:19322968251363632. doi: 10.1177/19322968251363632.
Diabetic foot ulcers (DFUs) affect 19% to 34% of individuals with diabetes during their lifetime and account for over one million nontraumatic lower-limb amputations annually. Standard care often fails to detect early, subclinical changes, leading to delayed intervention and high mortality rates. This review examines how artificial intelligence (AI) and machine learning (ML) can extract complex patterns from diverse data modalities to advance DFU care.
We examined AI/ML applications in DFU care across four domains: diagnosis (automated image and thermogram classification, biomechanical risk stratification), treatment optimization (AI-driven offloading prescriptions, tele-rehabilitation, molecularly informed wound care), prognosis (neural network and random forest models for risk assessment), and novel strategy development (generative AI, transcriptomic target discovery, wearable digital biomarkers).
Artificial intelligence/ML methodologies have demonstrated promising results in DFU image and thermogram analysis, with reported accuracies ranging from 81-97% across different studies. Biomechanical ML models show potential for dynamic risk stratification, and prognostic models achieve moderate performance with area under the curve values around 0.74-0.82. Generative AI approaches have shown promise for data augmentation, improving segmentation performance in limited datasets.
Despite promising advances, several challenges impede clinical translation, including data standardization, model explainability, regulatory compliance, clinical workflow integration, prospective validation, and equitable implementation. Collaborative efforts among clinicians, data scientists, regulators, and patients are essential to translate AI-driven innovations into routine DFU management, potentially reducing amputations and improving outcomes for this global health burden.
糖尿病足溃疡（DFU）在糖尿病患者一生中的影响率为19%至34%，每年导致超过100万例非创伤性下肢截肢。标准护理往往无法检测到早期的亚临床变化，导致干预延迟和高死亡率。本综述探讨了人工智能（AI）和机器学习（ML）如何从不同的数据模式中提取复杂模式，以推进DFU护理。
我们研究了DFU护理中四个领域的AI/ML应用：诊断（自动图像和热成像分类、生物力学风险分层）、治疗优化（AI驱动的减负处方、远程康复、分子指导的伤口护理）、预后（用于风险评估的神经网络和随机森林模型）以及新策略开发（生成式AI、转录组学靶点发现、可穿戴数字生物标志物）。
人工智能/ML方法在DFU图像和热成像分析中已显示出有前景的结果，不同研究报告的准确率在81%至97%之间。生物力学ML模型显示出动态风险分层的潜力，预后模型的表现中等，曲线下面积值约为0.74至0.82。生成式AI方法在数据增强方面显示出前景，可提高有限数据集中的分割性能。
尽管取得了有前景的进展，但仍有几个挑战阻碍了临床转化，包括数据标准化、模型可解释性、法规合规性、临床工作流程整合、前瞻性验证和公平实施。临床医生、数据科学家、监管机构和患者之间的合作对于将AI驱动的创新转化为常规DFU管理至关重要，这有可能减少截肢并改善这一全球健康负担的治疗结果。
