Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

Dee Zhen Lim; Melissa Yeo; Ariel Dahan; Bahman Tahayori; Hong Kuan Kok; Mohammad Abbasi-Rad; Julian Maingard; Numan Kutaiba; Jeremy Russell; Vincent Thijs; Ashu Jhamb; Ronil V Chandra; Mark Brooks; Christen Barras; Hamed Asadi

doi:10.1136/neurintsurg-2021-017858

Article info

Vascular neurology

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

Authors

Dee Zhen Lim Department of Radiology, Austin Health, Heidelberg, Victoria, Australia PubMed articles Google scholar articles
Melissa Yeo Melbourne Medical School, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia PubMed articles Google scholar articles
Ariel Dahan Department of Radiology, Austin Health, Heidelberg, Victoria, Australia PubMed articles Google scholar articles
Bahman Tahayori Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia PubMed articles Google scholar articles
Hong Kuan Kok Department of Radiology, Northern Health, Epping, Victoria, Australia School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia PubMed articles Google scholar articles
Mohammad Abbasi-Rad Programmer, Melbourne, Victoria, Australia PubMed articles Google scholar articles
Julian Maingard Department of Radiology, Monash Health, Clayton, Victoria, Australia Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia PubMed articles Google scholar articles
Numan Kutaiba Department of Radiology, Austin Health, Heidelberg, Victoria, Australia PubMed articles Google scholar articles
Jeremy Russell Department of Neurosurgery, Austin Health, Heidelberg, Victoria, Australia PubMed articles Google scholar articles
Vincent Thijs Department of Neurology, Austin Health, Heidelberg, Victoria, Australia Stroke Theme, Florey Neuroscience Institutes, Parkville, Victoria, Australia PubMed articles Google scholar articles
Ashu Jhamb Department of Radiology, St Vincent Health, Fitzroy, Victoria, Australia PubMed articles Google scholar articles
Ronil V Chandra Department of Radiology, Monash Health, Clayton, Victoria, Australia Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia PubMed articles Google scholar articles
Mark Brooks Department of Radiology, Austin Health, Heidelberg, Victoria, Australia School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia PubMed articles Google scholar articles
Christen Barras School of Medicine, University of Adelaide, Adelaide, South Australia, Australia South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia PubMed articles Google scholar articles
Hamed Asadi Department of Radiology, Austin Health, Heidelberg, Victoria, Australia School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia PubMed articles Google scholar articles

Correspondence to Dr Dee Zhen Lim, Department of Radiology, Austin Health, Heidelberg, Victoria, Australia; deezhenlim{at}gmail.com

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Citation

Lim DZ, Yeo M, Dahan A, et al

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study

Journal of NeuroInterventional Surgery 2022;14:799-803.

Publication history

Received June 5, 2021
Accepted August 5, 2021
First published August 23, 2021.

Online issue publication

July 14, 2022

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