Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), UMR S 1136, Sorbonne Universite, Paris, France.
Rheumatology Department, Hôpital Universitaire Pitié Salpêtrière, APHP, Paris, France.
RMD Open. 2019 Jul 18;5(2):e001004. doi: 10.1136/rmdopen-2019-001004. eCollection 2019.
To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).
A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.
Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000-5 billion) in RMDs, and 9.1 billion (range 100 000-200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).
Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.
评估大数据和人工智能（AI）在风湿和肌肉骨骼疾病（RMD）领域的应用现状。
于 2018 年 11 月在 PubMed MEDLINE 中进行了系统文献检索，使用的关键词包括大数据、AI 和 RMD。分析了所有以英文发表的原始报告。还在 RMD 以外的相同数量的文章中进行了镜像文献检索。收集分析的数据集数量、数据源和使用的统计方法（传统统计学、AI 或两者兼有）。对 RMD 内外的研究结果进行了比较分析。
与 RMD 相关的 567 篇文章中，有 55 篇符合纳入标准并进行了分析，同时还有 55 篇来自其他医学领域的文章。RMD 中的数据集平均数量为 7.46 亿（范围 2000 至 50 亿），RMD 以外的数据集平均数量为 91 亿（范围 10 万至 2000 亿）。数据源各不相同：在 RMD 中，26 个（47%）是临床数据，8 个（15%）是生物学数据，16 个（29%）是影像学数据。大数据分析中同时使用了传统和 AI 方法（分别在 RMD 中占 10 项（18%）和 45 项（82%），而在 RMD 以外的占 8 项（15%）和 47 项（85%））。在 RMD 中，AI 方法中有 97%为机器学习，在这些方法中，最常见的是人工神经网络（RMD 中有 20/44 篇文章）。
RMD 领域的大数据来源和类型多种多样，用于分析大数据的方法也各不相同。这些发现将为欧洲抗风湿病联盟的 RMD 大数据工作组提供信息。
