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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:

Routine Echocardiography and Artificial Intelligence Solutions
Mark J. Schuuring, Ivana Išgum, Bernard Cosyns, et al.
Frontiers in Cardiovascular Medicine (2021) Vol. 8
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

Advancements and applications of Artificial Intelligence in cardiology: Current trends and future prospects
David B. Olawade, Nicholas Aderinto, Gbolahan Olatunji, et al.
Journal of Medicine Surgery and Public Health (2024) Vol. 3, pp. 100109-100109
Open Access | Times Cited: 14
Reducing echocardiographic examination time through routine use of fully automated software: a comparative study of measurement and report creation time
Yukina Hirata, Yuka Nomura, Yoshihito Saijo, et al.
Journal of Echocardiography (2024) Vol. 22, Iss. 3, pp. 162-170
Open Access | Times Cited: 6
Clinical validation of an artificial intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in real time by a novel handheld ultrasound device
Stella‐Lida Papadopoulou, Vasileios Sachpekidis, V Kantartzi, et al.
European Heart Journal - Digital Health (2022) Vol. 3, Iss. 1, pp. 29-37
Open Access | Times Cited: 25
Assisted probe guidance in cardiac ultrasound: A review
Sofia Ferraz, Miguel Coimbra, João Pedrosa
Frontiers in Cardiovascular Medicine (2023) Vol. 10
Open Access | Times Cited: 12
Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome
Mitchel Molenaar, Berto J. Bouma, Folkert W. Asselbergs, et al.
European Heart Journal - Digital Health (2024) Vol. 5, Iss. 2, pp. 170-182
Open Access | Times Cited: 4
Deep learning framework for interpretable quality control of echocardiography video
Liwei Du, Wufeng Xue, Zhanru Qi, et al.
Medical Physics (2025)
Open Access
An efficient network with state space model under evidential training for fetal echocardiography standard view recognition
Changzhao Chen, Yiman Liu, Tongtong Liang, et al.
Medical & Biological Engineering & Computing (2025)
Closed Access
Use of Artificial Intelligence and Machine Learning in Critical Care Ultrasound
Marcus Peck, Hannah Conway
Critical Care Clinics (2025)
Closed Access
Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease
Mitchel Molenaar, Jasper L. Selder, Johny Nicolas, et al.
Current Cardiology Reports (2022) Vol. 24, Iss. 4, pp. 365-376
Open Access | Times Cited: 18
Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve
Corina Maria Vasile, Xavier Iriart
Diagnostics (2023) Vol. 13, Iss. 19, pp. 3137-3137
Open Access | Times Cited: 7
Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging
Ernst Wellnhofer
Frontiers in Cardiovascular Medicine (2022) Vol. 9
Open Access | Times Cited: 12
Transforming Echocardiography: The Role of Artificial Intelligence in Enhancing Diagnostic Accuracy and Accessibility
Kenya Kusunose
Internal Medicine (2024) Vol. 64, Iss. 3, pp. 331-336
Open Access | Times Cited: 2
Artificial Intelligence and Its Application in Cardiovascular Disease Management
Vigneshwaran Namasivayam, Nithyashree Senguttuvan, Venkatesan Saravanan, et al.
(2022), pp. 189-236
Closed Access | Times Cited: 10
Quantification of primary mitral regurgitation by echocardiography: A practical appraisal
Alexandre Altes, Emmanuelle Vermès, Franck Lévy, et al.
Frontiers in Cardiovascular Medicine (2023) Vol. 10
Open Access | Times Cited: 6
Automatic echocardiographic anomalies interpretation using a stacked residual-dense network model
Siti Nurmaini, Ade Iriani Sapitri, Bambang Tutuko, et al.
BMC Bioinformatics (2023) Vol. 24, Iss. 1
Open Access | Times Cited: 5
The impact of valvular heart disease in patients with chronic coronary syndrome
Mitchel Molenaar, Berto J. Bouma, Casper F. Coerkamp, et al.
Frontiers in Cardiovascular Medicine (2023) Vol. 10
Open Access | Times Cited: 4
Deep learning for automatic calcium detection in echocardiography
Luís B. Elvas, Sara Gomes, João C. Ferreira, et al.
BioData Mining (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 1
Automated echocardiography view classification and quality assessment with recognition of unknown views
Gino E. Jansen, Bob D. de Vos, Mitchel Molenaar, et al.
Journal of Medical Imaging (2024) Vol. 11, Iss. 05
Open Access | Times Cited: 1
Revealing Unforeseen Diagnostic Image Features With Deep Learning by Detecting Cardiovascular Diseases From Apical 4‐Chamber Ultrasounds
Li‐Hsin Cheng, Pablo B. J. Bosch, Rutger F. H. Hofman, et al.
Journal of the American Heart Association (2022) Vol. 11, Iss. 16
Open Access | Times Cited: 5
The Reliability of Automated Three-Dimensional Echocardiography-HeartModelA.I. Versus 2D Echocardiography Simpson Methods in Evaluation of Left Ventricle Volumes and Ejection Fraction in Patients With Left Ventricular Dysfunction
Nabil Naser, Ivan Stanković, Aleksandar Nešković
Medical Archives (2022) Vol. 76, Iss. 4, pp. 259-259
Open Access | Times Cited: 4
Künstliche Intelligenz in der pränatalen kardialen Diagnostik
J. Weichert, Amrei Welp, Jann Lennard Scharf, et al.
Der Gynäkologe (2021) Vol. 55, Iss. 1, pp. 22-31
Closed Access | Times Cited: 4
Editorial: Digital Solutions in Cardiology
Mark J. Schuuring, Alexandru N. Mischie, Enrico G. Caiani
Frontiers in Cardiovascular Medicine (2022) Vol. 9
Open Access | Times Cited: 3
The use of artificial intelligence for predicting postinfarction myocardial viability in echocardiographic images
Błażej Michalski, Sławomir Skonieczka, Michał Strzelecki, et al.
Cardiology Journal (2024) Vol. 31, Iss. 5, pp. 699-707
Open Access
Artificial Intelligence Echocardiography in Resource‐Limited Regions: Applications and Challenges
Izhan Hamza, Patricia A. Pellikka, Amer Abdulla, et al.
Echocardiography (2024) Vol. 41, Iss. 10
Closed Access
Temporally Consistent Segmentations from Sparsely Labeled Echocardiograms Using Image Registration for Pseudo-labels Generation
Matteo Tafuro, Gino E. Jansen, Ivana Išgum
Lecture notes in computer science (2023), pp. 195-204
Closed Access | Times Cited: 1
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