Elsevier

Clinical Imaging

Volume 69, January 2021, Pages 246-254
Clinical Imaging

Artificial Intelligence
Artificial intelligence in stroke imaging: Current and future perspectives

https://doi.org/10.1016/j.clinimag.2020.09.005Get rights and content

Highlights

  • Artificial Intelligence (AI) is a growing subfield of computer science that aims to mimic cognitive processes through a number of techniques.

  • Superficial AI, which is comprised of techniques that learn patterns with the use of labels of ‘ground truth’ data, have become a significant area of interest within the medical community. As images are, in essence, volumes of mineable data, radiology is a field of medicine particularly well suited to supervised AI techniques.

  • AI techniques could play pivotal roles in neuroradiology, particular in the diagnosis and management of time sensitive diseases such as stroke.

  • Several AI techniques, comprised of machine and deep learning methods, have shown incredible promise as an adjunct to a neuroradiologist's workflow, particularly within stroke imaging.

  • Although encouraging, there are data protection and medicolegal implications that must be considered for widespread clinical use of AI techniques.

Abstract

Artificial intelligence (AI) is a fast-growing research area in computer science that aims to mimic cognitive processes through a number of techniques. Supervised machine learning, a subfield of AI, includes methods that can identify patterns in high-dimensional data using labeled ‘ground truth’ data and apply these learnt patterns to analyze, interpret, or make predictions on new datasets. Supervised machine learning has become a significant area of interest within the medical community. Radiology and neuroradiology in particular are especially well suited for application of machine learning due to the vast amount of data that is generated. One devastating disease for which neuroimaging plays a significant role in the clinical management is stroke. Within this context, AI techniques can play pivotal roles for image-based diagnosis and management of stroke. This overview focuses on the recent advances of artificial intelligence methods – particularly supervised machine learning and deep learning – with respect to workflow, image acquisition and reconstruction, and image interpretation in patients with acute stroke, while also discussing potential pitfalls and future applications.

Introduction

Artificial intelligence (AI) is a subfield of computer science aiming at emulating human cognitive processes [1]. However, AI is an umbrella term that encompasses a number of techniques of varying complexity that can be used to solve different problems. One notable subfield of AI that has gained significant interest lately is machine learning (ML), including advanced deep learning methods [2]. Machine learning aims to automatically identify complex and high-dimensional patterns in existing datasets, which can then be used to make predictions or classifications based on new unseen datasets. Machine learning can be further subdivided into supervised and unsupervised machine learning as well as reinforcement learning. While all three types of machine learning can be useful for specific medical applications, supervised machine learning is arguably the most relevant machine learning type in this domain, especially with respect to precision medicine efforts. Briefly described, supervised machine learning methods are trained using “ground truth” labeled data to identify the corresponding patterns in the feature data [[3], [4], [5], [6]]. Examples of ML models include, for example, support vector machines (SVMs), decision trees, random forests (RFs), k-nearest neighbors, Bayesian methods, and generalized linear models (GLMs) [2]. Many of these methods can be used to solve classification (categorial outcome) as well as regression (continuous outcome) problems.

Lately, deep learning (DL) methods have gained increasing interest due to the superior performance achieved in many applications, including medicine, compared to other more traditional machine learning methods. Deep learning utilizes a specific type of a machine learning architecture, the so-called artificial neural networks, which loosely mimic how the human brain functions. Artificial neural networks consist of an input layer, multiple hidden layers, and one output layer (Fig. 1). Each layer consists of many neurons that are typically connected to neurons in the next layer [2,7,8]. Artificial neural networks use a set of quantitative variables as the input to the first layer, which are used together with corresponding labels to learn (optimize) the weights between the neurons in the network. Practically, these weights connecting the neurons are optimized through backpropagation and comparison of the predictions to the ground truth labels [2]. After optimization, the trained networks can be used to make predictions and classifications based on new unseen data.

Artificial neural networks cannot only use quantitative data as inputs, but also solve classification and regression problems on images that are used directly as an input to the network by using the intensity information of the pixels or voxels as input features. In this case, the artificial neural network is typically referred to as a convolutional neural network (CNN) [2,9]. Briefly described, the images or parts of images are analyzed in convolutional layers, which follow the input layer with the image and essentially contain filters that are optimized in the same way as the weights of the neural network using backpropagation to extract relevant texture information from the images. The quantified texture data is then fed through the hidden layers to make predictions on the image- or voxel-level (Fig. 2). CNNs have been shown to outperform classical machine learning techniques and image analysis methods in many computer vision problems. Radiology, and especially neuroradiology, is no exception to this, including applications for improved image quality, image acquisition and reconstruction, diagnostic accuracy, and treatment decision making for better patient care [1].

One pathology particularly amenable to machine learning advancements is stroke. Stroke is a leading cause of long term disability, morbidity, and mortality and timely intervention and increased sensitivity to subtle early findings can lead to significant benefits for the patients with respect to the clinical outcome [4]. Computed tomography (CT) and magnetic resonance imaging (MRI), including perfusion imaging (CT perfusion or perfusion-weighted MRI), play a critical role in diagnosis, intervention, and timely management of stroke [7]. Lately, AI has shown promise improving stroke management efficiency by incorporating its functionality in neuroimaging paradigm and workflow. This overview focuses on the recent advances of supervised ML and DL techniques, with respect to workflow, image acquisition and processing, and diagnosis pertinent to adult stroke management, while also discussing both the pitfalls and future directions within this subfield. This overview focusses specifically on CT and MRI applications due to the routine use of these neuroimaging techniques for diagnosis and treatment decision making, although a few supervised ML and DL techniques have been proposed for other neuroimaging techniques such as positron emission tomography and ultrasound imaging that can be of interest for specific stroke analyses too [[10], [11], [12]].

Section snippets

Imaging triage and workflow

Stroke is an acute neurological event where timely management is imperative. Thus, one area that has garnered interest is the prioritization of image interpretation by neuroradiologists. This is crucial since it ultimately expedites CT screening of hemorrhage or early signs of stroke through worklist prioritization. For example, Titano et al. [13] utilized a 3D CNN, which flags head CTs with high probability for acute neurologic events, including stroke, with an alarm mechanism for alerting

Image optimization

Another area in which AI has shown incredible progress lately is image optimization and quality improvement. These processes, which include fast image acquisition and reconstruction, denoising, artifact reduction, and resolution improvement, have considerable overlap in some instances. However, they will be discussed separately in this section as they pertain to stroke imaging.

Image analysis

Given the vast amount of data, time-sensitive nature of stroke, and potential for immediate clinical impact, image analysis has become arguably the most promising application of AI for stroke imaging. Image analysis encompasses detection and segmentation, classification, and prediction of stroke outcome using AI methods. These topics will be covered separately in this section despite exhibiting significant overlap in some cases.

Pitfalls/disadvantages

Supervised AI techniques have shown great promise for the support of image-based diagnosis support in acute ischemic stroke patients, but it is also essential to acknowledge the shortcomings and challenges, which prevent generalizability and clinical implementation. In this section, we address the main issues from data-related, medicolegal, and ethical perspectives.

Future direction

In this AI overview, we have covered a number of promising applications of AI pertinent to ischemic stroke diagnosis and treatment. Nevertheless, there is room for improvement and exciting opportunities ahead within this field. Integration of these AI platforms into clinical practice and how they affect neuroradiologists' workflow are the most pressing questions in the current environment. Areas of potential future exploration include AI integration in the electronic health record (EHR),

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