Vázquez-Marrufo M, Sarrias-Arrabal E, García-Torres M, Martín-Clemente R, Izquierdo G
Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, Spain.
Departamento de Psicología Experimental, Facultad de Psicología, Universidad de Sevilla, Sevilla, Spain.
Neurologia (Engl Ed). 2023 Oct;38(8):577-590. doi: 10.1016/j.nrleng.2020.10.013. Epub 2022 Jul 14.
The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.
We present a systematic review of the application of ML algorithms in MS.
We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.
After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.
人工智能的应用，尤其是自动学习或“机器学习”（ML），在众多科学、技术和临床学科中既是一项挑战，也是一个巨大的机遇。在多发性硬化症（MS）研究中的具体应用也不例外，并且构成了近年来人们日益感兴趣的一个领域。
我们对ML算法在MS中的应用进行了系统综述。
我们使用了PubMed搜索引擎（可免费访问MEDLINE医学数据库）来识别包含关键词“机器学习”和“多发性硬化症”的研究。我们排除了综述文章、非英语或西班牙语撰写的研究，以及主要是技术性且未专门应用于MS的研究。最终入选76篇文章，排除38篇。
经过综述过程，我们确定了ML在MS中的4个主要应用：1）对MS亚型进行分类；2）将MS患者与健康对照以及患有其他疾病的个体区分开来；3）预测疾病进展和对治疗干预的反应；4）其他应用。迄今为止的结果表明，ML算法在临床环境和MS研究中都可能为卫生专业人员提供巨大支持。
