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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:

Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores
Nora F. Dengler, Vince I. Madai, Meike Unteroberdörster, et al.
Neurosurgical Review (2021) Vol. 44, Iss. 5, pp. 2837-2846
Open Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Machine Learning in Action: Stroke Diagnosis and Outcome Prediction
Shraddha Mainali, Marin E. Darsie, Keaton S. Smetana
Frontiers in Neurology (2021) Vol. 12
Open Access | Times Cited: 97
XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage
Ruoran Wang, Jing Zhang, Baoyin Shan, et al.
Neuropsychiatric Disease and Treatment (2022) Vol. Volume 18, pp. 659-667
Open Access | Times Cited: 55
Analysis of Cerebral Spinal Fluid Drainage and Intracranial Pressure Peaks in Patients with Subarachnoid Hemorrhage
Anton Früh, Peter Truckenmüller, David Wasilewski, et al.
Neurocritical Care (2024) Vol. 41, Iss. 2, pp. 619-631
Open Access | Times Cited: 5
Applicable artificial intelligence for brain disease: A survey
Chenxi Huang, Jian Wang, Shuihua Wang‎, et al.
Neurocomputing (2022) Vol. 504, pp. 223-239
Open Access | Times Cited: 22
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
Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients
Jeffrey R. Vitt, Shraddha Mainali
Seminars in Neurology (2024) Vol. 44, Iss. 03, pp. 342-356
Closed Access | Times Cited: 4
Comparison of prediction for short-term and long-term outcomes in patients with aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis
Yang Zhang, Chunxiang Yan, Guangyu Lu, et al.
Neurosurgical Review (2025) Vol. 48, Iss. 1
Closed Access
Artificial intelligence in stroke risk assessment and management via retinal imaging
Parsa Khalafi, Soroush Morsali, Sana Hamidi, et al.
Frontiers in Computational Neuroscience (2025) Vol. 19
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
Implications of Artificial Intelligence in Stroke Intervention and Care
Jyoti Yadav, Aditya More, Bijoyani Ghosh, et al.
iRadiology (2025)
Open Access
Development and validation of a preliminary clinical support system for measuring the probability of incident 2-year (pre)frailty among community-dwelling older adults: A prospective cohort study
Qinqin Liu, Liming Yang, Zhuming Shi, et al.
International Journal of Medical Informatics (2023) Vol. 177, pp. 105138-105138
Closed Access | Times Cited: 7
Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data
Nils Schweingruber, Jan Phillip Bremer, Anton Wiehe, et al.
Journal of Clinical Monitoring and Computing (2024) Vol. 38, Iss. 5, pp. 1175-1186
Open Access | Times Cited: 2
Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies
Jewel Sengupta, Robertas Alzbutas
BioMed Research International (2022) Vol. 2022, Iss. 1
Open Access | Times Cited: 11
Enhancing the prediction for shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage using a machine learning approach
Dietmar Frey, Adam Hilbert, Anton Früh, et al.
Neurosurgical Review (2023) Vol. 46, Iss. 1
Open Access | Times Cited: 6
Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms
Nico Stroh, Harald Stefanits, Alexander Maletzky, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 6
Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan
Sergio García-García, Santiago Cepeda, Dominik Müller, et al.
Brain Sciences (2023) Vol. 14, Iss. 1, pp. 10-10
Open Access | Times Cited: 6
An accurate prognostic prediction for aneurysmal subarachnoid hemorrhage dedicated to patients after endovascular treatment
Lu Han, Gaici Xue, Sisi Li, et al.
Therapeutic Advances in Neurological Disorders (2022) Vol. 15
Open Access | Times Cited: 9
Deep-Learning-Based Stroke Screening Using Skeleton Data from Neurological Examination Videos
Taeho Lee, Eun‐Tae Jeon, Jin‐Man Jung, et al.
Journal of Personalized Medicine (2022) Vol. 12, Iss. 10, pp. 1691-1691
Open Access | Times Cited: 8
Should individual timeline and serial CT/MRI panels of all patients be presented in acute brain insult cohorts? A pilot study of 45 patients with decompressive craniectomy after aneurysmal subarachnoid hemorrhage
Anniina H. Autio, Juho Paavola, Joona Tervonen, et al.
Acta Neurochirurgica (2023) Vol. 165, Iss. 11, pp. 3299-3323
Open Access | Times Cited: 4
Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review update
A. Parekh, Samarth Satish, Louise Dulhanty, et al.
Journal of NeuroInterventional Surgery (2023), pp. jnis-021107
Closed Access | Times Cited: 4
External Validation of a Neural Network Model in Aneurysmal Subarachnoid Hemorrhage: A Comparison With Conventional Logistic Regression Models
James Feghali, Shahab Aldin Sattari, Elizabeth E. Wicks, et al.
Neurosurgery (2022) Vol. 90, Iss. 5, pp. 552-561
Closed Access | Times Cited: 7
Machine learning-based identification of contrast-enhancement phase of computed tomography scans
Siddharth Guha, Abdalla Ibrahim, Qian Wu, et al.
PLoS ONE (2024) Vol. 19, Iss. 2, pp. e0294581-e0294581
Open Access | Times Cited: 1
Preoperatively-determined Red Distribution Width (RDW) predicts prolonged length of stay after single-level spinal fusion in elderly patients
Anton Früh, Dietmar Frey, Adam Hilbert, et al.
Brain and Spine (2024) Vol. 4, pp. 102827-102827
Open Access | Times Cited: 1
Evaluating Deep Learning Techniques for Detecting Aneurysmal Subarachnoid Hemorrhage: A Comparative Analysis of Convolutional Neural Network and Transfer Learning Models
Mustafa Umut Etli, Muhammet Sinan Başarslan, Eyüp Varol, et al.
World Neurosurgery (2024) Vol. 187, pp. e807-e813
Closed Access | Times Cited: 1
Plasma Neurofilament Light Chain as a Biomarker for Poor Outcome After Aneurysmal Subarachnoid Hemorrhage
Homeyra Labib, Maud A. Tjerkstra, Charlotte E. Teunissen, et al.
World Neurosurgery (2024) Vol. 189, pp. e238-e252
Open Access | Times Cited: 1
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