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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:

Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modeling of Hemodynamics
Pavlo Yevtushenko, Leonid Goubergrits, Lina Gundelwein, et al.
IEEE Journal of Biomedical and Health Informatics (2021) Vol. 26, Iss. 4, pp. 1815-1825
Closed Access | Times Cited: 24

Showing 24 citing articles:

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond
Amirhossein Arzani, Jianxun Wang, Michael S. Sacks, et al.
Annals of Biomedical Engineering (2022) Vol. 50, Iss. 6, pp. 615-627
Closed Access | Times Cited: 60
Deep Learning for Computational Hemodynamics: A Brief Review of Recent Advances
Amirtahà Taebi
Fluids (2022) Vol. 7, Iss. 6, pp. 197-197
Open Access | Times Cited: 29
Inverse problems in blood flow modeling: A review
D Nolte, C Bertoglio
International Journal for Numerical Methods in Biomedical Engineering (2022) Vol. 38, Iss. 8
Open Access | Times Cited: 26
Deep learning-based real-time prediction of coronary artery blood flow pressure from computed tomography angiography
Yang Yang, Bao Li, Chuanqi Wen, et al.
Physics of Fluids (2025) Vol. 37, Iss. 2
Closed Access
Applications of Computational Fluid Dynamics in Congenital Heart Disease: A Review
A. Dave, Raquel dos Santos, Usmaan Siddiqi, et al.
Journal of Cardiovascular Development and Disease (2025) Vol. 12, Iss. 2, pp. 70-70
Open Access
Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
Endrit Pajaziti, Javier Montalt‐Tordera, Claudio Capelli, et al.
PLoS Computational Biology (2023) Vol. 19, Iss. 4, pp. e1011055-e1011055
Open Access | Times Cited: 12
Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science
Ifra Saifi, Basharat Ahmad Bhat, Syed Suhail Hamdani, et al.
Journal of Biomolecular Structure and Dynamics (2023) Vol. 42, Iss. 12, pp. 6523-6541
Closed Access | Times Cited: 11
Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks
Jakob Versnjak, Pavlo Yevtushenko, Titus Küehne, et al.
Frontiers in Physiology (2024) Vol. 15
Open Access | Times Cited: 3
USING CONVOLUTIONAL NEURAL NETWORK-BASED SEGMENTATION FOR IMAGE-BASED COMPUTATIONAL FLUID DYNAMICS SIMULATIONS OF BRAIN ANEURYSMS: INITIAL EXPERIENCE IN AUTOMATED MODEL CREATION
Mostafa Rezaeitaleshmahalleh, Zonghan Lyu, Nan Mu, et al.
Journal of Mechanics in Medicine and Biology (2023) Vol. 23, Iss. 04
Closed Access | Times Cited: 7
Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes
Anas Tahir, Onur Mutlu, Fayçal Bensaali, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 14, pp. 4774-4774
Open Access | Times Cited: 7
Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine
Pavlo Yevtushenko, Leonid Goubergrits, Benedikt Franke, et al.
Frontiers in Cardiovascular Medicine (2023) Vol. 10
Open Access | Times Cited: 6
Machine Learning Identification Framework of Hemodynamics of Blood Flow in Patient-Specific Coronary Arteries with Abnormality
Mohammad Farajtabar, Morsal Momeni Larimi, Mohit Biglarian, et al.
Journal of Cardiovascular Translational Research (2022) Vol. 16, Iss. 3, pp. 722-737
Closed Access | Times Cited: 8
Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance
Yili Feng, Ruisen Fu, Hao Sun, et al.
Artificial Intelligence in Medicine (2023) Vol. 147, pp. 102744-102744
Closed Access | Times Cited: 4
Numerical Method for Geometrical Feature Extraction and Identification of Patient-Specific Aorta Models in Pediatric Congenital Heart Disease
Alex G. Kuchumov, Olga V. Doroshenko, Mikhail V. Golub, et al.
Mathematics (2023) Vol. 11, Iss. 13, pp. 2871-2871
Open Access | Times Cited: 2
Deep graph convolutional neural network for one-dimensional hepatic vascular haemodynamic prediction
Weiqng Zhang, Shuaifeng Shi, Quan Qi
bioRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
Machine Learning Based Extraction of Boundary Conditions from Doppler Echo Images for Patient Specific Coarctation of the Aorta: Computational Fluid Dynamics Study
Vincent Milimo Masilokwa Punabantu, Malebogo Ngoepe, Amit Kumar Mishra, et al.
Mathematical and Computational Applications (2024) Vol. 29, Iss. 5, pp. 71-71
Open Access
Investigation of Relationship between Hemodynamic and Morphometric Characteristics of Aortas in Pediatric Patients
Olga V. Doroshenko, Alex G. Kuchumov, Mikhail V. Golub, et al.
Journal of Clinical Medicine (2024) Vol. 13, Iss. 17, pp. 5141-5141
Open Access
Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks
Wenyuan Song, David Frakes, Lakshmi Prasad Dasi
Annals of Biomedical Engineering (2024)
Closed Access
Impact of cardiovascular magnetic resonance in single ventricle physiology: a narrative review
Inga Voges, Dominik Daniel Gabbert, Daniela Panakova, et al.
Cardiovascular Diagnosis and Therapy (2024) Vol. 14, Iss. 6, pp. 1161-1175
Open Access
Computational approaches for mechanobiology in cardiovascular development and diseases
Aaron L. Brown, Zachary A. Sexton, Zinan Hu, et al.
Current topics in developmental biology/Current Topics in Developmental Biology (2024), pp. 19-50
Closed Access
Deep Learning during burn prehospital care: An evolving perspective
Mohammad Vakili Ojarood, Ramyar Farzan, Seyed Mostafa Mohsenizadeh, et al.
Burns (2024) Vol. 50, Iss. 5, pp. 1349-1351
Closed Access
A Learning-based Acceleration Framework for Transient Hemodynamic Simulations
Q Zhang, Xiaohu Zhou, Xiao‐Liang Xie, et al.
Procedia Computer Science (2024) Vol. 250, pp. 136-142
Open Access
Machine learning model for preoperative assessment of ultrasound diathermy with implants
Hsiu-Chin Hsu, Siang-Rong Lin, H.-C. Chen, et al.
International Journal of Thermal Sciences (2023) Vol. 193, pp. 108499-108499
Closed Access
Machine-Learning Identification of Hemodynamics in Coronary Arteries in the Presence of Stenosis.
Mohammad Farajtabar, Mohit Biglarian, Morteza Miansari
arXiv (Cornell University) (2021)
Closed Access
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