Nathan A Shlobin1, Ammad A Baig2, Muhammad Waqas2, Tatsat R Patel3, Rimal H Dossani2, Megan Wilson4, Justin M Cappuzzo2, Adnan H Siddiqui5, Vincent M Tutino6, Elad I Levy7. 1. Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. 2. Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA. 3. Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, New York, USA. 4. Southern Methodist University, Dallas, Texas, USA. 5. Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA; Jacobs Institute, Buffalo, New York, USA. 6. Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, New York, USA; Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, New York, USA. 7. Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute at Kaleida Health, Buffalo, New York, USA; Jacobs Institute, Buffalo, New York, USA. Electronic address: elevy@ubns.com.
Abstract
BACKGROUND: Optimal outcomes after large-vessel occlusion (LVO) stroke are highly dependent on prompt diagnosis, effective communication, and treatment, making LVO an attractive avenue for the application of artificial intelligence (AI), specifically machine learning (ML). Our objective is to conduct a systematic review to describe existing AI applications for LVO strokes, delineate its effectiveness, and identify areas for future AI applications in stroke treatment and prognostication. METHODS: A systematic review was conducted by searching the PubMed, Embase, and Scopus databases. After deduplication, studies were screened by title and abstract. Full-text studies were screened for final inclusion based on prespecified inclusion and exclusion criteria. Relevant data were extracted from each study. RESULTS: Of 11,512 resultant articles, 40 were included. Of 30 studies with reported ML algorithms, the most commonly used ML algorithms were convolutional neural networks in 10 (33.3%), support vector machines in 10 (33.0%), and random forests in 9 (30.0%). Studies examining triage favored direct transport to a stroke center and predicted improved outcomes. ML techniques proved vastly accurate in identifying LVO on computed tomography. Applications of AI to patient selection for thrombectomy are lacking, although some studies determine individual patient eligibility for endovascular treatment with high accuracy. ML algorithms have reasonable accuracy in predicting clinical and angiographic outcomes and associated factors. CONCLUSIONS: AI has shown promise in the diagnosis and triage of patients with acute stroke. However, the role of AI in the management and prognostication remains limited and warrants further research to help in decision support.
BACKGROUND: Optimal outcomes after large-vessel occlusion (LVO) stroke are highly dependent on prompt diagnosis, effective communication, and treatment, making LVO an attractive avenue for the application of artificial intelligence (AI), specifically machine learning (ML). Our objective is to conduct a systematic review to describe existing AI applications for LVO strokes, delineate its effectiveness, and identify areas for future AI applications in stroke treatment and prognostication. METHODS: A systematic review was conducted by searching the PubMed, Embase, and Scopus databases. After deduplication, studies were screened by title and abstract. Full-text studies were screened for final inclusion based on prespecified inclusion and exclusion criteria. Relevant data were extracted from each study. RESULTS: Of 11,512 resultant articles, 40 were included. Of 30 studies with reported ML algorithms, the most commonly used ML algorithms were convolutional neural networks in 10 (33.3%), support vector machines in 10 (33.0%), and random forests in 9 (30.0%). Studies examining triage favored direct transport to a stroke center and predicted improved outcomes. ML techniques proved vastly accurate in identifying LVO on computed tomography. Applications of AI to patient selection for thrombectomy are lacking, although some studies determine individual patient eligibility for endovascular treatment with high accuracy. ML algorithms have reasonable accuracy in predicting clinical and angiographic outcomes and associated factors. CONCLUSIONS: AI has shown promise in the diagnosis and triage of patients with acute stroke. However, the role of AI in the management and prognostication remains limited and warrants further research to help in decision support.
Authors: Gianluca Brugnara; Ulf Neuberger; Mustafa A Mahmutoglu; Martha Foltyn; Christian Herweh; Simon Nagel; Silvia Schönenberger; Sabine Heiland; Christian Ulfert; Peter Arthur Ringleb; Martin Bendszus; Markus A Möhlenbruch; Johannes A R Pfaff; Philipp Vollmuth Journal: Stroke Date: 2020-10-12 Impact factor: 7.914
Authors: Salim S Virani; Alvaro Alonso; Hugo J Aparicio; Emelia J Benjamin; Marcio S Bittencourt; Clifton W Callaway; April P Carson; Alanna M Chamberlain; Susan Cheng; Francesca N Delling; Mitchell S V Elkind; Kelly R Evenson; Jane F Ferguson; Deepak K Gupta; Sadiya S Khan; Brett M Kissela; Kristen L Knutson; Chong D Lee; Tené T Lewis; Junxiu Liu; Matthew Shane Loop; Pamela L Lutsey; Jun Ma; Jason Mackey; Seth S Martin; David B Matchar; Michael E Mussolino; Sankar D Navaneethan; Amanda Marma Perak; Gregory A Roth; Zainab Samad; Gary M Satou; Emily B Schroeder; Svati H Shah; Christina M Shay; Andrew Stokes; Lisa B VanWagner; Nae-Yuh Wang; Connie W Tsao Journal: Circulation Date: 2021-01-27 Impact factor: 29.690
Authors: Raphael Meier; Paula Lux; B Med; Simon Jung; Urs Fischer; Jan Gralla; Mauricio Reyes; Roland Wiest; Richard McKinley; Johannes Kaesmacher Journal: Radiol Artif Intell Date: 2019-09-11