BACKGROUND AND PURPOSE:The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.

On the basis of animal models, glymphatic flow disruption is hypothesized to be a factor in the development of Alzheimer's disease. We report the first quantitative study of glymphatic flow in man, combining intrathecal administration of gadobutrol with serial T1 mapping to produce contrast concentration maps up to 3 days postinjection, demonstrating performing a quantitative study using the techniques described feasibility and providing data on pharmacokinetics.

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...