Deep into the Brain: Artificial Intelligence in Stroke Imaging

Eun-Jae Lee, Yong-Hwan Kim, Namkug Kim, Dong-Wha Kang, Eun-Jae Lee, Yong-Hwan Kim, Namkug Kim, Dong-Wha Kang

Abstract

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.

Keywords: Artificial intelligence; Machine learning; Stroke.

Conflict of interest statement

The authors have no financial conflicts of interest.

Figures

Figure 1.
Figure 1.
Schematic workflow of supervised machine learning.
Figure 2.
Figure 2.
Hyper-plane of support vector machine. (A) Hyper-plane fails to distinguish the groups. (B) Hyper-plane distinguishes the groups but with a small margin. (C) Hyper-plane distinguishes the groups with the maximum margin.
Figure 3.
Figure 3.
Schematic representation of neural network. (A) Artificial neural network with a single hidden layer. All nodes are fully connected between layers. (B) Deep neural network with two hidden layers. Deep learning has multiple hidden layers. (C) Recurrent neural network with a single hidden layer architecture. Nodes in the hidden layer have a directed cycle. (D) Convolutional neural network. Weighted connections are indicated with the same color in convolutional hidden layers.

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