Artificial Intelligence and Acute Stroke Imaging

Abstract

Artificial intelligence technology is a rapidly expanding field with many applications in acute stroke imaging, including ischemic and hemorrhage subtypes. Early identification of acute stroke is critical for initiating prompt intervention to reduce morbidity and mortality. Artificial intelligence can help with various aspects of the stroke treatment paradigm, including infarct or hemorrhage detection, segmentation, classification, large vessel occlusion detection, Alberta Stroke Program Early CT Score grading, and prognostication. In particular, emerging artificial intelligence techniques such as convolutional neural networks show promise in performing these imaging-based tasks efficiently and accurately. The purpose of this review is twofold: first, to describe AI methods and available public and commercial platforms in stroke imaging, and second, to summarize the literature of current artificial intelligence-driven applications for acute stroke triage, surveillance, and prediction. ABBREVIATIONS: AI ¼ artificial intelligence; ANN ¼ artificial neural network; AUC ¼ area under the curve; CNN ¼ convolutional neural network; DL ¼ deep learning; ICC ¼ intraclass correlation coefficient; ICH ¼ intracranial hemorrhage; LVO ¼ large vessel occlusion; ML ¼ machine learning; MRP ¼ MR perfusion; RF ¼ random forest; SVM ¼ support vector machine S troke is the second leading cause of death worldwide with an annual mortality of about 5.5 million. 1,2 In the United States, nearly 800,000 people have a stroke annually, and the economic burden of stroke is estimated at $34 billion per year. 3 Morbidity is high, with more than half of patients with stroke left chronically disabled. 2 Neuroimaging is an important tool for the detection, characterization, and prognostication of acute strokes, including ischemic and hemorrhagic subtypes. Artificial intelligence (AI) technology is a rapidly burgeoning field, providing a promising avenue for fast and efficient imaging analysis. 4 AI applications for imaging of acute cerebrovascular disease have been implemented, including tools for triage, quantification, surveillance, and prediction. This review aims to summarize the current landscape of AIdriven applications for acute cerebrovascular disease assessment focusing primarily on deep learning (DL) methods. OVERVIEW OF AI Although AI, machine learning (ML), and DL are used interchangeably, these in fact represent subdisciplines. Specifically, DL is a subset of ML, and ML is a subset of AI (Fig 1). Broadly, AI uses computers to perform tasks that typically require human knowledge. ML, a subset of AI, uses statistical approaches to enable machines to optimize outcome prediction as they are exposed to data and train computers for pattern recognition, a task generally requiring human intelligence. 5 ML offers several potential advantages over visual inspection by human experts, including objective and quantitative evaluation, the ability to detect subtle voxel-level patterns, speed, and large-scale implementation. Feature selection, classifi...

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients

“…An overview of several segmentation methods with a comparison to the present study is shown in Table 5 of the Supplementary Appendix. AI-based image analysis is increasingly applied in clinical practice, especially in the field of acute stroke [13]. Despite this evolution, there are still a limited number of AI-based applications commercially available to assess ICHs in patients with acute stroke.…”

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

“…The hierarchy of AI methods is as follows: 1. deep learning (DL) is a subset of 2. representation learning (RL), which is a subset of 3. machine learning (ML), which represents a subset of 4. AI [16]. Classic medical algorithms consist of three steps: 1. preprocessing of medical images, 2.…”

“…Large vessel occlusion (LVO) detection is essential for identifying potential candidates who could benefit from mechanical thrombectomy. CTA is typically the preferred technique for large vessel occlusion detection, but noncontrast-enhanced CT (NCCT) scans can also be used to localize the site of occlusion (Figure 2), and decrease the necessary amount of contrast media and reserve it only for angiography during recanalization procedure [16].…”

“…Large vessel occlusion (LVO) detection is essential for identifying potential candidates who could benefit from mechanical thrombectomy. CTA is typically the preferred technique for large vessel occlusion detection, but noncontrast-enhanced CT (NCCT) scans can also be used to localize the site of occlusion (Figure 2), and decrease the necessary amount of contrast media and reserve it only for angiography during recanalization procedure [16]. Similarly, when analyzing 1453 patients with suspected acute stroke, Olive-Gadea et al found that a deep learning algorithm could even accurately identify large vessel occlusion on NCCTs, with an AUC of 0.87, and that this identification is improved (AUC of 0.91) when adding clinical parameters to AI system (such as NIHSS and time from symptom onset) [58].…”

Detection of Unilateral Arm Paresis after Stroke by Wearable Accelerometers and Machine Learning

“…In the field of stroke care, artificial intelligence and wearable sensors have primarily been used to identify risk factors such as detection of atrial fibrillation using smartwatches [ 5 , 6 ]—reviewed in [ 7 ]—and blood pressure estimation based on photoplethysmography to assess hypertension [ 8 ]. Within stroke imaging, artificial intelligence has been used to perform several imaging processing tasks, reviewed in [ 9 , 10 ]. However, most of these applications are based on data gathered with a human interpreter in mind, such as the visual aspects of a skin lesion or the digital reconstruction of a chest X-ray.…”