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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:

Machine Learning in Acute Ischemic Stroke Neuroimaging
Haris Kamal, Víctor A. López, Sunil A. Sheth
Frontiers in Neurology (2018) Vol. 9
Open Access | Times Cited: 110

Showing 1-25 of 110 citing articles:

Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier
Asit Subudhi, Manasa Dash, Sukanta Sabut
Journal of Applied Biomedicine (2019) Vol. 40, Iss. 1, pp. 277-289
Closed Access | Times Cited: 169
Artificial intelligence as an emerging technology in the current care of neurological disorders
Urvish Patel, Arsalan Anwar, Sidra Saleem, et al.
Journal of Neurology (2019) Vol. 268, Iss. 5, pp. 1623-1642
Closed Access | Times Cited: 148
A systematic review of machine learning models for predicting outcomes of stroke with structured data
Wenjuan Wang, Martin Kiik, Niels Peek, et al.
PLoS ONE (2020) Vol. 15, Iss. 6, pp. e0234722-e0234722
Open Access | Times Cited: 144
Tuberculous meningitis: progress and remaining questions
Julie Huynh, Joseph Donovan, Nguyen Hoan Phu, et al.
The Lancet Neurology (2022) Vol. 21, Iss. 5, pp. 450-464
Closed Access | Times Cited: 77
Applications of Deep Learning to Neuro-Imaging Techniques
Guangming Zhu, Bin Jiang, Liz Tong, et al.
Frontiers in Neurology (2019) Vol. 10
Open Access | Times Cited: 128
Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset
Thippa Reddy Gadekallu, Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, et al.
Multimedia Tools and Applications (2020) Vol. 81, Iss. 29, pp. 41429-41453
Closed Access | Times Cited: 82
Predicting Infarct Core From Computed Tomography Perfusion in Acute Ischemia With Machine Learning: Lessons From the ISLES Challenge
Arsany Hakim, Sören Christensen, Stefan Winzeck, et al.
Stroke (2021) Vol. 52, Iss. 7, pp. 2328-2337
Open Access | Times Cited: 66
Isolation Forest-Voting Fusion-Multioutput: A stroke risk classification method based on the multidimensional output of abnormal sample detection
Hai He, Haibo Yang, Francesco Mercaldo, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 253, pp. 108255-108255
Closed Access | Times Cited: 11
Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI
Yannan Yu, Danfeng Guo, Min Lou, et al.
IEEE Transactions on Biomedical Engineering (2017) Vol. 65, Iss. 9, pp. 2058-2065
Closed Access | Times Cited: 82
Artificial intelligence in stroke imaging: Current and future perspectives
Vivek Yedavalli, Elizabeth Tong, Dann Martin, et al.
Clinical Imaging (2020) Vol. 69, pp. 246-254
Closed Access | Times Cited: 63
Automatic detection of stroke lesion from diffusion-weighted imaging via the improved YOLOv5
Shannan Chen, Jinfeng Duan, Hong Wang, et al.
Computers in Biology and Medicine (2022) Vol. 150, pp. 106120-106120
Closed Access | Times Cited: 35
Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review
Liyuan Cui, Zhiyuan Fan, Yingjian Yang, et al.
BioMed Research International (2022) Vol. 2022, pp. 1-15
Open Access | Times Cited: 33
Topics and trends in artificial intelligence assisted human brain research
Xieling Chen, Juan Chen, Gary Cheng, et al.
PLoS ONE (2020) Vol. 15, Iss. 4, pp. e0231192-e0231192
Open Access | Times Cited: 49
Detection and vascular territorial classification of stroke on diffusion-weighted MRI by deep learning
Yusuf Kenan Çetinoğlu, İlker Özgür Koska, Muhsin Engin Uluç, et al.
European Journal of Radiology (2021) Vol. 145, pp. 110050-110050
Closed Access | Times Cited: 36
Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies
İlker Özgür Koska, M. Alper Selver, Fazıl Gelal, et al.
Japanese Journal of Radiology (2024) Vol. 42, Iss. 9, pp. 962-972
Closed Access | Times Cited: 5
MRI radiomic features-based machine learning approach to classify ischemic stroke onset time
Yiqun Zhang, Ao-Fei Liu, Fengyuan Man, et al.
Journal of Neurology (2021) Vol. 269, Iss. 1, pp. 350-360
Closed Access | Times Cited: 29
Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT
JoonNyung Heo, Youngno Yoon, Hyun Jin Han, et al.
European Radiology (2023) Vol. 34, Iss. 6, pp. 3840-3848
Closed Access | Times Cited: 11
Enhancing ischemic stroke management: leveraging machine learning models for predicting patient recovery after Alteplase treatment
Babak Khorsand, Atena Vaghf, Vahide Salimi, et al.
Brain Injury (2025), pp. 1-7
Open Access
State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators
Ali Haider Bangash, Jayro Toledo, Muhammed Amir Essibayi, et al.
Neurosurgical Review (2025) Vol. 48, Iss. 1
Open Access
Computational Image Analysis of Nonenhanced Computed Tomography for Acute Ischaemic Stroke: A Systematic Review
Paul Mikhail, Michael Gia Duy Le, Grant Mair
Journal of Stroke and Cerebrovascular Diseases (2020) Vol. 29, Iss. 5, pp. 104715-104715
Open Access | Times Cited: 29
Artificial intelligence and neuroscience: An update on fascinating relationships
Nishanth Gopinath
Process Biochemistry (2022) Vol. 125, pp. 113-120
Closed Access | Times Cited: 18
Association of Retinal Biomarkers With the Subtypes of Ischemic Stroke and an Automated Classification Model
Zhouwei Xiong, William Robert Kwapong, Shouyue Liu, et al.
Investigative Ophthalmology & Visual Science (2024) Vol. 65, Iss. 8, pp. 50-50
Open Access | Times Cited: 3
Case studies and use cases of deep learning for biomedical applications
M. Manivannan, T. V. Padmavathy, Balamurugan Balusamy
Elsevier eBooks (2025), pp. 179-191
Closed Access
Patterns of Infarction on MRI in Patients With Acute Ischemic Stroke and Cardio-Embolism: A Systematic Review and Meta-Analysis
Angelos Sharobeam, Leonid Churilov, Mark Parsons, et al.
Frontiers in Neurology (2020) Vol. 11
Open Access | Times Cited: 27
Potentials and caveats of AI in hybrid imaging
Lalith Kumar Shiyam Sundar, Otto Muzik, Irène Buvat, et al.
Methods (2020) Vol. 188, pp. 4-19
Open Access | Times Cited: 26
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