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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:

Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning
Ananya Singh, Robert J.H. Miller, Yuka Otaki, et al.
JACC. Cardiovascular imaging (2022) Vol. 16, Iss. 2, pp. 209-220
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models
Ahmed Salih, Ilaria Boscolo Galazzo, Polyxeni Gkontra, et al.
Circulation Cardiovascular Imaging (2023) Vol. 16, Iss. 4
Open Access | Times Cited: 40
Deep Learning for Cardiovascular Imaging
Ramsey M. Wehbe, Aggelos K. Katsaggelos, Kristian J. Hammond, et al.
JAMA Cardiology (2023) Vol. 8, Iss. 11, pp. 1089-1089
Closed Access | Times Cited: 23
Deep learning on medical image analysis
Jiaji Wang, Shuihua Wang‎, Yudong Zhang
CAAI Transactions on Intelligence Technology (2024)
Open Access | Times Cited: 8
Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies
Ioannis D. Apostolopoulos, Νικόλαος Παπανδριανός, Anna Feleki, et al.
EJNMMI Physics (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 17
A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging
Mélanie Champendal, Henning Müller, John O. Prior, et al.
European Journal of Radiology (2023) Vol. 169, pp. 111159-111159
Open Access | Times Cited: 16
Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends
Robert J.H. Miller, Piotr J. Slomka
Seminars in Nuclear Medicine (2024) Vol. 54, Iss. 5, pp. 648-657
Closed Access | Times Cited: 5
A review of evaluation approaches for explainable AI with applications in cardiology
Ahmed Salih, Ilaria Boscolo Galazzo, Polyxeni Gkontra, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 9
Open Access | Times Cited: 4
Artificial intelligence for nuclear cardiology: Perspectives and challenges
C Gilbert, Alec T Chunta, Robert J.H. Miller
International Journal of Cardiovascular Sciences (2025) Vol. 38
Open Access
Emerging Applications of Positron Emission Tomography in Coronary Artery Disease
Anna Błach, Jacek Kwieciński
Journal of Personalized Medicine (2025) Vol. 15, Iss. 3, pp. 100-100
Open Access
Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction
Anna M Marcinkiewicz, Wenhao Zhang, Aakash Shanbhag, et al.
npj Digital Medicine (2025) Vol. 8, Iss. 1
Open Access
Artificial Intelligence in Nuclear Cardiac Imaging: Novel Advances, Emerging Techniques, and Recent Clinical Trials
Ilana Golub, Abhinav Thummala, Tyler Morad, et al.
Journal of Clinical Medicine (2025) Vol. 14, Iss. 6, pp. 2095-2095
Open Access
Artificial Intelligence in Nuclear Cardiology
Robert J.H. Miller
Cardiology Clinics (2023) Vol. 41, Iss. 2, pp. 151-161
Closed Access | Times Cited: 9
The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0)
Robert J.H. Miller, Mark A. Lemley, Aakash Shanbhag, et al.
Journal of Nuclear Medicine (2024), pp. jnumed.124.268292-jnumed.124.268292
Closed Access | Times Cited: 2
Innovative Attention-Based Explainable Feature-Fusion VGG19 Network for Characterising Myocardial Perfusion Imaging SPECT Polar Maps in Patients with Suspected Coronary Artery Disease
Ioannis D. Apostolopoulos, Νικόλαος Παπαθανασίου, Νικόλαος Παπανδριανός, et al.
Applied Sciences (2023) Vol. 13, Iss. 15, pp. 8839-8839
Open Access | Times Cited: 5
Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction
Anna M. Michałowska, Wenhao Zhang, Aakash Shanbhag, et al.
(2024)
Closed Access | Times Cited: 1
Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics
Cong He, Fangye Wu, Lin-feng Fu, et al.
BioMedical Engineering OnLine (2024) Vol. 23, Iss. 1
Open Access | Times Cited: 1
Prognostic value of the stress‐hyperglycaemia ratio in patients with moderate‐to‐severe coronary artery calcification: Insights from a large cohort study
Zhangyu Lin, Yanjun Song, Sheng Yuan, et al.
Diabetes Obesity and Metabolism (2024) Vol. 26, Iss. 11, pp. 4933-4944
Closed Access | Times Cited: 1
Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk Measurement Techniques and Assessment of Their Effectiveness (Review)
B. I. Geltser, I. G. Domzhalov, K. I. Shakhgeldyan, et al.
Sovremennye tehnologii v medicine (2024) Vol. 16, Iss. 4, pp. 61-61
Open Access | Times Cited: 1
The Role of Deep Learning in Myocardial Perfusion Imaging for Diagnosis and Prognosis: A Systematic Review
Xueping Hu, Han Zhang, Federico Caobelli, et al.
iScience (2024) Vol. 27, Iss. 12, pp. 111374-111374
Open Access | Times Cited: 1
Next Step for Hybrid PET-CT Imaging
Leslee J. Shaw, Ron Blankstein
JACC. Cardiovascular imaging (2023) Vol. 16, Iss. 5, pp. 688-690
Open Access | Times Cited: 2
The Role of Positron Emission Tomography in Advancing the Understanding of the Pathogenesis of Heart and Vascular Diseases
Anna Błach, Jacek Kwieciński
Diagnostics (2023) Vol. 13, Iss. 10, pp. 1791-1791
Open Access | Times Cited: 2
Current status and future directions in artificial intelligence for nuclear cardiology
Robert J. H. Miller, Piotr J. Slomka
Expert Review of Cardiovascular Therapy (2024) Vol. 22, Iss. 8, pp. 367-378
Closed Access
Deep Learning Artificial Intelligence for Single Photon Emission Computed Tomography: A Comprehensive Overview and Future Perspectives
Rongyu Cui, Xin Xie, Long Fan, et al.
(2024), pp. 416-421
Closed Access
Application of Artificial Intelligence in Cardiology: A Bibliometric Analysis
Abubakar I Sidik, R. N. Komarov, Sidique Gawusu, et al.
Cureus (2024)
Open Access
Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis
Vedat Çiçek, Ezgi Hasret Kozan Cikirikci, Mert Babaoğlu, et al.
EJNMMI Research (2024) Vol. 14, Iss. 1
Open Access
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