Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature review
This review identifies trends and methodological shortcomings in the application of machine learning to DTI for researchers working on brain imaging analysis.
This paper reviewed 148 studies published between 2010 and 2019 that applied machine learning to Diffusion Tensor Imaging (DTI) of the human brain. The review found that classification was the most common application (n=114), with Support Vector Machines (SVM) being the most frequently used model (n=93), and that most studies used small cohorts (less than 100) and lacked external validation.
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.