Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature review

Abstract

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.

Combining Multiple Indices of Diffusion Tensor Imaging Can Better Differentiate Patients with Traumatic Brain Injury from Healthy Subjects

“…Previous studies used SVM and other ML statistical techniques with different DTI parameters and obtained variable levels of accuracy. [37][38][39][40][41][42][43][44]66,67 Vergara et al applied an SVM approach in mTBI patients, using FA and resting-state functional network connectivity (rsFNC) as features in their models. 38 They reported that rsFNC within the default mode network provided the best classification accuracy (84%), followed by FA (75.5%).…”

“…[34][35][36] Saha et al recently reviewed 148 papers published between January 2010 and December 2019 related to the human brain, DTI, and ML, and identified five studies that involved the classification of TBI patients versus healthy controls. [37][38][39][40][41][42] Various DTI measures were extracted and used for voxel-based analysis, tractography, and generating connectivity matrices. Support vector machine (SVM) a supervised ML technique were used in all five studies.…”

Transfer Learning and Magnetic Resonance Imaging Techniques for the Deep Neural Network-Based Diagnosis of Early Cognitive Decline and Dementia

“…In the last decade, a significant number of studies used machine learning methods for medical diagnosis [6,7]. The highest popularity among traditional machine learning has been gained by approaches that use support vector machines (SVM), support vector regression (SVR), and random forest (RF) classifiers [6].…”

“…In the last decade, a significant number of studies used machine learning methods for medical diagnosis [6,7]. The highest popularity among traditional machine learning has been gained by approaches that use support vector machines (SVM), support vector regression (SVR), and random forest (RF) classifiers [6]. Advances in deep neural networks have opened a wide diagnostic opportunity in the classification and processing of medical imaging data offering additional benefits [7].…”