Leveraging artificial intelligence in ischemic stroke imaging - 18/06/22

Doi : 10.1016/j.neurad.2021.05.001

Omid Shafaat ^a , Joshua D. Bernstock ^b , Amir Shafaat ^c , Vivek S. Yedavalli ^a , Galal Elsayed ^d , Saksham Gupta ^b , Ehsan Sotoudeh ^e , Haris I. Sair ^a,^f , David M. Yousem ^a , Houman Sotoudeh ^g,⁎
^a The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA
^b Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA
^c Department of Mechanical Engineering, Arak University of Technology, Daneshgah St, 38181-41167 Arak, Iran
^d Department of Neurosurgery, University of Alabama at Birmingham, 1960 6th Ave. S., Birmingham, AL 35233, USA
^e Department of Surgery, Iranian Hospital in Dubai, P.O.BOX: 2330, Al-Wasl Road, Dubai 2330, UAE
^f Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, 600 North Wolfe Street, Baltimore, MD 21287, USA
^g Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA

^⁎Corresponding author at: Division of Neuroradiology, Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA.Division of NeuroradiologyDepartment of RadiologyUniversity of Alabama at Birmingham619 19th St SBirminghamAL35294USA

Abstract

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics’ background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients’ selection for treatment.

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Abbreviations : AI, rtPA, ML, SVM, ANN, CNN, RF, GBM, DT, XGB, ASPECT, MCA, DWI, AUC, LVO, MLP, kNN, PPV, NPV, ATLAS, 3D, mRS, ICH, pCASL, ROC, rs-fMRI, ASTRAL, THRIVE, SVM+PCA, NB, LR, DNN, fMRI, RFNN, AE, DTI, FLAIR

Keywords : Stroke, Brain ischemia, Artificial intelligence, Machine learning, Neural network

Plan

Introduction

A brief introduction to artificial intelligence

Automated diagnosis

ASPECT (Alberta Stroke Program Early CT) Score

Segmentation

Patient selection for intervention

Prognosis prediction

Conclusion

Funding acknowledgments

Disclosure/Conflict of interest

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Vol 49 - N° 4

P. 343-351 - juin 2022 Retour au numéro

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Leveraging artificial intelligence in ischemic stroke imaging - 18/06/22

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