Deep Learning and Neurology: A Systematic Review

Abstract

Deciphering the massive volume of complex electronic data that has been compiled by hospital systems over the past decades has the potential to revolutionize modern medicine, as well as present significant challenges. Deep learning is uniquely suited to address these challenges, and recent advances in techniques and hardware have poised the field of medical machine learning for transformational growth. The clinical neurosciences are particularly well positioned to benefit from these advances given the subtle presentation of symptoms typical of neurologic disease. Here we review the various domains in which deep learning algorithms have already provided impetus for change-areas such as medical image analysis for the improved diagnosis of Alzheimer's disease and the early detection of acute neurologic events; medical image segmentation for quantitative evaluation of neuroanatomy and vasculature; connectome mapping for the diagnosis of Alzheimer's, autism spectrum disorder, and attention deficit hyperactivity disorder; and mining of microscopic electroencephalogram signals and granular genetic signatures. We additionally note important challenges in the integration of deep learning tools in the clinical setting and discuss the barriers to tackling the challenges that currently exist.

Artificial Intelligence and Computational Approaches for Epilepsy

“… 29 These learning tasks are executed in two main types: supervised versus unsupervised learnings. 29 – 33 Supervised learning is the approach that trains using labeled data, that is, data whose target outputs have already been known. It is mainly used for classification or regression purposes and its algorithm includes k-nearest neighbor (k-NN), linear/logistic regression, naïve Bayes, random forest, and support vector machine (SVM).…”

“…It is mainly used for classification or regression purposes and its algorithm includes k-nearest neighbor (k-NN), linear/logistic regression, naïve Bayes, random forest, and support vector machine (SVM). 29 – 33 On the other hand, unsupervised learning is the approach that trains using unlabeled data. It is mainly used for clustering or association analysis purposes and its algorithm includes k-means, k-medoids, fuzzy C-means, Gaussian mixture, hidden Markov model.…”

“…It is mainly used for clustering or association analysis purposes and its algorithm includes k-means, k-medoids, fuzzy C-means, Gaussian mixture, hidden Markov model. 29 – 33 Artificial neural network (ANN) is another machine learning algorithm that performs the learning task by mimicking the brain nervous system, including neuronal dynamics and synaptic plasticity, and is widely used in both supervised and unsupervised learning schemes. 29 , 31 , 33 – 36 If there are more than two hidden layers constituting ANN, it is specifically called deep neural network (DNN), and using those models to achieve the learning function is called deep learning.…”

“… 29 – 33 Artificial neural network (ANN) is another machine learning algorithm that performs the learning task by mimicking the brain nervous system, including neuronal dynamics and synaptic plasticity, and is widely used in both supervised and unsupervised learning schemes. 29 , 31 , 33 – 36 If there are more than two hidden layers constituting ANN, it is specifically called deep neural network (DNN), and using those models to achieve the learning function is called deep learning. 33 Deep learning approach has a significant advantage in automatically discovering discriminative features from data and learning them compared to traditional machine learning approach, which requires an additional process to extract the features manually and apply them as inputs.…”

“… 29 , 31 , 33 – 36 If there are more than two hidden layers constituting ANN, it is specifically called deep neural network (DNN), and using those models to achieve the learning function is called deep learning. 33 Deep learning approach has a significant advantage in automatically discovering discriminative features from data and learning them compared to traditional machine learning approach, which requires an additional process to extract the features manually and apply them as inputs. 31 , 33 , 36 , 37 Thus, recently, it has been aggressively used in various applications even if it needs higher computational power.…”