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Deep Learning Applications for Acute Stroke Management
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AI
DOI：10.1002/ana.26435.png)
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摘要
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Brain imaging is essential to the clinical care of patients with stroke, a leading cause of disability and death worldwide. Whereas advanced neuroimaging techniques offer opportunities for aiding acute stroke management, several factors, including time delays, inter-clinician variability, and lack of systemic conglomeration of clinical information, hinder their maximal utility. Recent advances in deep machine learning (DL) offer new strategies for harnessing computational medical image analysis to inform decision making in acute stroke. We examine the current state of the field for DL models in stroke triage. First, we provide a brief, clinical practice-focused primer on DL. Next, we examine real-world examples of DL applications in pixel-wise labeling, volumetric lesion segmentation, stroke detection, and prediction of tissue fate postintervention. We evaluate recent deployments of deep neural networks and their ability to automatically select relevant clinical features for acute decision making, reduce inter-rater variability, and boost reliability in rapid neuroimaging assessments, and integrate neuroimaging with electronic medical record (EMR) data in order to support clinicians in routine and triage stroke management. Ultimately, we aim to provide a framework for critically evaluating existing automated approaches, thus equipping clinicians with the ability to understand and potentially apply DL approaches in order to address challenges in clinical practice. ANN NEUROL 2022
Keyword:
ISCHEMIC-STROKE
OUTCOME PREDICTION
INTRACRANIAL HEMORRHAGE
ARTIFICIAL-INTELLIGENCE
INTRAARTERIAL THERAPY
LESION SEGMENTATION
RANDOMIZED-TRIAL
EARLY CT
THROMBECTOMY
DIFFUSION
期刊
IF:
7.7
论文数:
8.5K
被引数:
3.6W
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引用论文
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NEUROIMAGE-CLINICAL
OAAI
NEUROIMAGE-CLINICALIF3.6
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Prediction of Stroke Onset Is Improved by Relative Fluid-Attenuated Inversion Recovery and Perfusion Imaging Compared to the Visual Diffusion-Weighted Imaging/Fluid-Attenuated Inversion Recovery MismatchSTROKEOAAI
STROKEIF8.9
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STROKEIF8.9
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