McMullen Eric P, Grewal Rajan S, Storm Kyle, Mbuagbaw Lawrence, Maretzki Maxine R, Larché Maggie J
Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
School of Health, University of Waterloo, Waterloo, ON, Canada.
J Scleroderma Relat Disord. 2024 Oct;9(3):171-177. doi: 10.1177/23971983241253718. Epub 2024 May 24.
This scoping review aims to summarize the existing literature on how machine learning can be used to impact systemic sclerosis diagnosis, management, and treatment. Following Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines, Embase, Web of Science, Medline (PubMed), IEEE Xplore, and ACM Digital Library were searched from inception to 3 March 2024, for primary literature reporting on machine learning models in any capacity regarding scleroderma. Following robust triaging, 11 retrospective studies were included in this scoping review. Three studies focused on the diagnosis of scleroderma to influence preferred management and nine studies on treatment and predicting treatment response to scleroderma. Nine studies used supervision in their machine learning model training; two used supervised and unsupervised training and one used solely unsupervised training. A total of 817 patients were included in the data sets. Seven of the included articles used patients from the United States, one from Belgium, two from Japan, and two from China. Although currently limited to retrospective studies, the results indicate that machine learning modeling may have a role in early diagnosis, management, therapeutic decision-making, and in the development of future therapies for systemic sclerosis. Prospective studies examining the use of machine learning in clinical practice are recommended to confirm the utility of machine learning in patients with systemic sclerosis.
本综述旨在总结关于机器学习如何用于影响系统性硬化症诊断、管理和治疗的现有文献。遵循系统评价和Meta分析扩展的范围综述（PRISMA-ScR）报告指南，检索了Embase、科学网、医学索引（PubMed）、IEEE Xplore和ACM数字图书馆，从创刊至2024年3月3日，查找关于机器学习模型以任何形式用于硬皮病的原始文献。经过严格筛选，本综述纳入了11项回顾性研究。三项研究聚焦于硬皮病的诊断以影响首选管理方式，九项研究关注治疗以及预测硬皮病的治疗反应。九项研究在其机器学习模型训练中使用了监督学习；两项使用了监督学习和无监督学习，一项仅使用了无监督学习。数据集中总共纳入了817例患者。纳入的文章中有七篇使用了来自美国的患者，一篇来自比利时，两篇来自日本，两篇来自中国。尽管目前仅限于回顾性研究，但结果表明机器学习建模可能在系统性硬化症的早期诊断、管理、治疗决策以及未来治疗方法的开发中发挥作用。建议进行前瞻性研究以检验机器学习在临床实践中的应用，以确认机器学习在系统性硬化症患者中的效用。
