College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China.
Department of Encephalopathy, The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin, China.
Front Endocrinol (Lausanne). 2024 Apr 29;15:1325434. doi: 10.3389/fendo.2024.1325434. eCollection 2024.
Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies.
This review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics.
A meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as "Machine Learning," "Diabetic Foot," "Diabetic Foot Ulcers," "Diabetic Foot Care," "Artificial Intelligence," and "Predictive Modeling." This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved.
ML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care.
The review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.
糖尿病足并发症给全球医疗系统带来了巨大压力，是糖尿病患者发病率和死亡率的主要原因。虽然传统的诊断和治疗方法存在局限性，但机器学习 (ML) 技术的出现开创了一个新时代，有望通过提高诊断准确性和制定针对性的治疗策略来彻底改变糖尿病足的护理。
本综述旨在探讨 ML 在管理糖尿病足并发症方面的变革性影响，强调通过利用医学成像、生物标志物检测和临床生物力学方面的发展，提高诊断准确性和治疗方法的潜力。
我们仔细检索了 PubMed、Scopus 和 Google Scholar 数据库，以确定截至 2024 年 3 月发表的相关文章。精心制定了搜索策略，使用了“机器学习”、“糖尿病足”、“糖尿病足溃疡”、“糖尿病足护理”、“人工智能”和“预测建模”等关键词的组合。本综述深入分析了构成 ML 的基本原理和算法，并特别强调了它们在医学科学中的相关性，特别是在糖尿病足病理学的专业领域。通过引入说明性的案例研究和示意图，本综述努力阐明涉及的复杂计算方法。
ML 已被证明在从复杂数据集提取关键见解方面非常有价值，提高了糖尿病足管理的诊断精度和治疗计划。本综述强调了 ML 在临床决策中的功效，通过对 ML 算法在糖尿病足护理中的预后评估和诊断应用中的比较分析得到了证实。
本综述以对 ML 在糖尿病足护理领域应用的轨迹进行前瞻性评估结束。我们相信，尽管存在计算限制和伦理考虑等挑战，但 ML 仍然处于彻底改变全球适用和注重精度的糖尿病足并发症治疗模式的前沿。这种技术演变预示着治疗和增强患者护理的前所未有的可能性。
