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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning
Hidehisa Nishi, Naoya Oishi, Akira Ishii, et al.
Stroke (2019) Vol. 50, Iss. 9, pp. 2379-2388
Open Access | Times Cited: 118

Showing 1-25 of 118 citing articles:

Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning
Gianluca Brugnara, Ulf Neuberger, Mustafa Ahmed Mahmutoglu, et al.
Stroke (2020) Vol. 51, Iss. 12, pp. 3541-3551
Open Access | Times Cited: 139
Clot-Based Radiomics Predict a Mechanical Thrombectomy Strategy for Successful Recanalization in Acute Ischemic Stroke
Jérémy Hofmeister, Gianmarco Bernava, Andrea Rosi, et al.
Stroke (2020) Vol. 51, Iss. 8, pp. 2488-2494
Open Access | Times Cited: 89
Artificial Intelligence and Acute Stroke Imaging
Jennifer E. Soun, Daniel Chow, Masaki Nagamine, et al.
American Journal of Neuroradiology (2020) Vol. 42, Iss. 1, pp. 2-11
Open Access | Times Cited: 84
Posterior National Institutes of Health Stroke Scale Improves Prognostic Accuracy in Posterior Circulation Stroke
Fana Alemseged, Alessandro Rocco, Francesco Arba, et al.
Stroke (2021) Vol. 53, Iss. 4, pp. 1247-1255
Open Access | Times Cited: 78
Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review
Giuseppe Miceli, Maria Grazia Basso, Giuliana Rizzo, et al.
Biomedicines (2023) Vol. 11, Iss. 4, pp. 1138-1138
Open Access | Times Cited: 28
Decreased Quantitative Cerebral Blood Volume Is Associated With Poor Outcomes in Large Core Patients
Vivek Yedavalli, Hamza Salim, Janet Mei, et al.
Stroke (2024) Vol. 55, Iss. 10, pp. 2409-2419
Closed Access | Times Cited: 12
AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI
Isuru Senadheera, Prasad Hettiarachchi, Brendon S. Haslam, et al.
Sensors (2024) Vol. 24, Iss. 20, pp. 6585-6585
Open Access | Times Cited: 9
Deep Learning–Derived High-Level Neuroimaging Features Predict Clinical Outcomes for Large Vessel Occlusion
Hidehisa Nishi, Naoya Oishi, Akira Ishii, et al.
Stroke (2020) Vol. 51, Iss. 5, pp. 1484-1492
Open Access | Times Cited: 70
Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease
Hui Shi, Dong Yang, Kaichen Tang, et al.
Clinical Nutrition (2021) Vol. 41, Iss. 1, pp. 202-210
Closed Access | Times Cited: 50
Artificial Intelligence for Large-Vessel Occlusion Stroke: A Systematic Review
Nathan A. Shlobin, Ammad A. Baig, Muhammad Waqas, et al.
World Neurosurgery (2021) Vol. 159, pp. 207-220.e1
Open Access | Times Cited: 43
Machine learning and acute stroke imaging
Sunil A. Sheth, Luca Giancardo, Marco Colasurdo, et al.
Journal of NeuroInterventional Surgery (2022) Vol. 15, Iss. 2, pp. 195-199
Open Access | Times Cited: 38
Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review
Ela M. Akay, Adam Hilbert, Benjamin Gregory Carlisle, et al.
Stroke (2023) Vol. 54, Iss. 6, pp. 1505-1516
Open Access | Times Cited: 22
Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis
Ting-Hsuan Sun, Chia‐Chun Wang, Ya-Lun Wu, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
The value of CT-based radiomics in predicting hemorrhagic transformation in acute ischemic stroke patients without recanalization therapy
Huang Yin-hui, Zhenjie Chen, Ya‐Fang Chen, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access | Times Cited: 6
Using machine learning to predict stroke‐associated pneumonia in Chinese acute ischaemic stroke patients
Xiang Li, Min Wu, Chao Sun, et al.
European Journal of Neurology (2020) Vol. 27, Iss. 8, pp. 1656-1663
Closed Access | Times Cited: 43
Leveraging artificial intelligence in ischemic stroke imaging
Omid Shafaat, Joshua D. Bernstock, Amir Shafaat, et al.
Journal of Neuroradiology (2021) Vol. 49, Iss. 4, pp. 343-351
Closed Access | Times Cited: 39
Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation
Chaojin Chen, Dong Yang, Shilong Gao, et al.
Respiratory Research (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 36
Outcome Prediction Models for Endovascular Treatment of Ischemic Stroke: Systematic Review and External Validation
Femke C.C. Kremers, Esmée Venema, Martijne H.C. Duvekot, et al.
Stroke (2021) Vol. 53, Iss. 3, pp. 825-836
Open Access | Times Cited: 33
Machine learning prediction of hematoma expansion in acute intracerebral hemorrhage
Satoru Tanioka, Tetsushi Yago, Katsuhiro Tanaka, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 28
Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning
Xueyang Wang, Jinhao Lyu, Zhihua Meng, et al.
CNS Neuroscience & Therapeutics (2023) Vol. 29, Iss. 4, pp. 1024-1033
Open Access | Times Cited: 14
Incorporating Artificial Intelligence Into Stroke Care and Research
Lingling Ding, Chelsea Liu, Zixiao Li, et al.
Stroke (2020) Vol. 51, Iss. 12
Open Access | Times Cited: 37
Machine Learning Risk Prediction for Incident Heart Failure in Patients With Atrial Fibrillation
Yasuhiro Hamatani, Hidehisa Nishi, Moritake Iguchi, et al.
JACC Asia (2022) Vol. 2, Iss. 6, pp. 706-716
Open Access | Times Cited: 20
An explainable machine learning model for predicting the outcome of ischemic stroke after mechanical thrombectomy
Zhelv Yao, Chenglu Mao, Zhihong Ke, et al.
Journal of NeuroInterventional Surgery (2022) Vol. 15, Iss. 11, pp. 1136-1141
Open Access | Times Cited: 20
Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis
Mingfen Wu, Kefu Yu, Zhigang Zhao, et al.
Heliyon (2024) Vol. 10, Iss. 2, pp. e24230-e24230
Open Access | Times Cited: 4
PREDICTIVE MODELS OF CLINICAL OUTCOME OF ENDOVASCULAR TREATMENT FOR ANTERIOR CIRCULATION STROKE USING MACHINE LEARNING.
Benoı̂t Clément, Aymeric Rouchaud, Gentric Jean-Christophe, et al.
Journal of Neuroscience Methods (2025), pp. 110376-110376
Open Access
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