  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.
