The role of machine learning in advancing diabetic foot: a review

作者：Huifang Guan, Ying Wang, Ping Niu, Yuxin Zhang, Yanjiao Zhang, Runyu Miao, Xinyi Fang, Ruiyang Yin, Shuang Zhao, Jun Liu, Jiaxing Tian · 发表于：Frontiers in Endocrinology · 年份：2024 · DOI：10.3389/fendo.2024.1325434 · 被引用次数：49 · 研究领域：Diabetic Foot Ulcer Assessment and Management、Lower Extremity Biomechanics and Pathologies、Peripheral Artery Disease Management

Background: 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. Objective: 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. Methods: 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 e...