PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■糖尿病足并发症给全世界的医疗系统带来了巨大的压力，是糖尿病患者发病和死亡的主要原因。虽然诊断和治疗这些疾病的传统方法面临着局限性，机器学习（ML）技术的出现预示着一个新时代，通过提高精度和量身定制的治疗策略，提供革命性的糖尿病足护理的承诺。
■这篇综述旨在探讨ML对糖尿病足并发症管理的转化影响，强调其通过利用医学成像的发展来提高诊断准确性和治疗方法的潜力，生物标志物检测，和临床生物力学。
■在PubMed中进行了细致的文献检索，Scopus,和谷歌学者数据库，以确定截至2024年3月发表的相关文章。搜索策略是精心制作的，采用关键词的组合，如“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。“这篇综述对构成ML的基本原理和算法进行了深入分析，特别强调它们与医学科学的相关性，特别是在糖尿病足病理学的专门领域内。通过结合说明性案例研究和示意图，审查努力阐明所涉及的复杂的计算方法。
■ML已被证明在从复杂数据集获得关键见解方面是无价的，提高糖尿病足管理的诊断精度和治疗计划。这篇综述强调了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的比较分析来强调。
■该评论最终对糖尿病足部护理领域中ML应用的轨迹进行了前瞻性评估。我们相信，尽管存在诸如计算限制和道德考虑等挑战，ML仍然处于全球适用且以精度为导向的糖尿病足并发症管理的革命性治疗范式的最前沿。这种技术发展预示着前所未有的治疗可能性和增强患者护理的机会。
■这篇综述旨在探讨ML对糖尿病足并发症管理的转化影响，强调其通过利用医学成像的发展来提高诊断准确性和治疗方法的潜力，生物标志物检测，和临床生物力学。
■在PubMed中进行了细致的文献检索，Scopus,和谷歌学者数据库，以确定截至2024年3月发表的相关文章。搜索策略是精心制作的，采用关键词的组合，如“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。“这篇综述对构成ML的基本原理和算法进行了深入分析，特别强调它们与医学科学的相关性，特别是在糖尿病足病理学的专门领域内。通过结合说明性案例研究和示意图，审查努力阐明所涉及的复杂的计算方法。
■ML已被证明在从复杂数据集获得关键见解方面是无价的，提高糖尿病足管理的诊断精度和治疗计划。这篇综述强调了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的比较分析来强调。
■该评论最终对糖尿病足部护理领域中ML应用的轨迹进行了前瞻性评估。我们相信，尽管存在诸如计算限制和道德考虑等挑战，ML仍然处于全球适用且以精度为导向的糖尿病足并发症管理的革命性治疗范式的最前沿。这种技术发展预示着前所未有的治疗可能性和增强患者护理的机会。
