论文标题
使用人脑的扩散张量成像的机器学习应用：PubMed文献评论
Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature review
论文作者
论文摘要
我们进行了PubMed搜索，以发现2010年1月至2019年12月之间发表的148篇论文，与人脑，扩散张量成像（DTI）和机器学习（ML）有关。这些研究集中于健康队列（n = 15），心理健康疾病（n = 25），肿瘤（n = 19），创伤（n = 5），痴呆症（n = 24），发育障碍（n = 5），运动障碍（n = 9），运动障碍（n = 9），其他神经系统疾病（其他神经疾病（n = 27），异常非疾病，不及时疾病和多个脑部疾病，而没有=（nontoces not in = notectation and = note = note = note = note = note = note = note = note = note nose = nover（non）上述类别的组合（n = 12）。使用来自DTI信息的患者分类是最常见的（n = 114）进行的ML应用。研究的大量研究（n = 93）使用了支持向量机（SVM）作为分类ML模型的首选选择。近年来（2018-2019）的大量出版物（31/44）继续使用SVM，支持向量回归和随机森林，这些森林是传统ML的一部分。尽管在各种健康状况（包括健康）上进行了许多类型的应用，但大多数研究都是基于小队列（小于100）的，并且没有对测试集进行独立/外部验证。
We performed a PubMed search to find 148 papers published between January 2010 and December 2019 related to human brain, Diffusion Tensor Imaging (DTI), and Machine Learning (ML). The studies focused on healthy cohorts (n = 15), mental health disorders (n = 25), tumor (n = 19), trauma (n = 5), dementia (n = 24), developmental disorders (n = 5), movement disorders (n = 9), other neurological disorders (n = 27), miscellaneous non-neurological disorders, or without stating the disease of focus (n = 7), and multiple combinations of the aforementioned categories (n = 12). Classification of patients using information from DTI stands out to be the most commonly (n = 114) performed ML application. A significant number (n = 93) of studies used support vector machines (SVM) as the preferred choice of ML model for classification. A significant portion (31/44) of publications in the recent years (2018-2019) continued to use SVM, support vector regression, and random forest which are a part of traditional ML. Though many types of applications across various health conditions (including healthy) were conducted, majority of the studies were based on small cohorts (less than 100) and did not conduct independent/external validation on test sets.
