Use of artificial intelligence in CT image evaluation in stroke patients – current options

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Authors: Z. Trabalková ^1,2; M. Števík ¹; J. Sýkora ^1,2; M. Vorčák ¹; K. Zeleňák ¹
Authors‘ workplace: Rádiologická klinika JLF UK a UNM, Martin, SR ¹; Rádiologická klinika LF UP a FN Olomouc, ČR ²
Published in: Cesk Slov Neurol N 2024; 87(1): 32-40
Category: Review Article
doi: https://doi.org/10.48095/cccsnn202432

Overview

Artificial intelligence and its rapid development represent one of the most important technological advances of the current decade. It affects almost all aspects of life, including medicine. Artificial intelligence is widely applied in neuroradiology, particularly in stroke diagnosis. The primary purpose of its application in this area is to accelerate the interpretation process, increase diagnostic accuracy, and help to select the treatment strategy. Clinicians involved in the initial management of a stroke patient should be familiar with the technical principles and possible use of artificial intelligence in neuroimaging, and they should know the strengths and weaknesses of the technology. This article briefly presents methods of artificial intelligence used in visual data processing. The main goal of the publication is to present particular automated analyses used in the interpretation of diagnostic information taken from CT images. CT is the primary choice in stroke diagnostics for most medical departments. The presented analyses are a calculation of the ASPECT score and detection of a hyperdense artery sign from non-contrast CT scans, identification of large vessel occlusion and collateral score evaluation from CTA, and creation of perfusion maps from CT perfusion.

Keywords:

deep learning – machine learning – ischemic stroke – large vessel occlusion – artifi cial intelligence

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