机器学习在骨关节炎研究中的应用：系统文献综述。
Use of machine learning in osteoarthritis research: a systematic literature review.
机构信息
Department of Rheumatology, Hôpital Saint-Antoine, Assistance Publique - Hôpitaux de Paris (AP-HP), Centre de Recherche Saint-Antoine, Inserm UMRS_938, Assistance Publique - Hôpitaux de Paris (AP-HP), Sorbonne Universite, Paris, France.
Bakar Computational Health Science Institute, University of California, San Francisco, California, USA.
出版信息
RMD Open. 2022 Mar;8(1). doi: 10.1136/rmdopen-2021-001998.
OBJECTIVE
The aim of this systematic literature review was to provide a comprehensive and exhaustive overview of the use of machine learning (ML) in the clinical care of osteoarthritis (OA).
METHODS
A systematic literature review was performed in July 2021 using MEDLINE PubMed with key words and MeSH terms. For each selected article, the number of patients, ML algorithms used, type of data analysed, validation methods and data availability were collected.
RESULTS
From 1148 screened articles, 46 were selected and analysed; most were published after 2017. Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. The number of patients included ranged from 18 to 5749. Overall, 35% of the articles described the use of deep learning And 74% imaging analyses. A total of 85% of the articles involved knee OA and 15% hip OA. No study investigated hand OA. Most of the studies involved the same cohort, with data from the OA initiative described in 46% of the articles and the MOST and Cohort Hip and Cohort Knee cohorts in 11% and 7%. Data and source codes were described as publicly available respectively in 54% and 22% of the articles. External validation was provided in only 7% of the articles.
CONCLUSION
This review proposes an up-to-date overview of ML approaches used in clinical OA research and will help to enhance its application in this field.
目的
本系统文献回顾的目的是全面详尽地概述机器学习（ML）在骨关节炎（OA）临床护理中的应用。
方法
2021 年 7 月，使用 MEDLINE PubMed 进行了系统文献回顾，使用了关键词和 MeSH 术语。对于每一篇选定的文章，收集了患者数量、使用的 ML 算法、分析的数据类型、验证方法和数据可用性。
结果
从 1148 篇筛选出的文章中，有 46 篇被选中并进行了分析；大多数文章是在 2017 年后发表的。有 12 篇文章与诊断有关，7 篇与预测有关，4 篇与表型有关，12 篇与严重程度有关，11 篇与进展有关。纳入的患者数量从 18 到 5749 不等。总体而言，35%的文章描述了深度学习的使用，74%的文章进行了影像学分析。85%的文章涉及膝关节 OA，15%涉及髋关节 OA。没有研究涉及手部 OA。大多数研究涉及同一队列，46%的文章中描述了 OA 倡议的数据，11%和 7%的文章中描述了 MOST 和 Cohort Hip 和 Cohort Knee 队列的数据。分别有 54%和 22%的文章将数据和源代码描述为公开可用。只有 7%的文章提供了外部验证。
结论
本综述提出了 ML 方法在临床 OA 研究中的最新概述，将有助于提高其在该领域的应用。
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