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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:

AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging
Robert J.H. Miller, Aakash Shanbhag, Aditya Killekar, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 16

Showing 16 citing articles:

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: 9
Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study
Robert J H Miller, Jirong Yi, Aakash Shanbhag, et al.
European Heart Journal (2025)
Open Access | Times Cited: 1
AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening
Anna M Marcinkiewicz, Mikołaj Buchwald, Aakash Shanbhag, et al.
Radiology (2024) Vol. 312, Iss. 3
Closed Access | Times Cited: 5
One Scan, Multiple Insights: A Review of AI-Driven Biomarker Imaging and Composite Measure Detection in Lung Cancer Screening
Sunil Verma, Leander Maerkisch, Alberto Paderno, et al.
Meta-Radiology (2025), pp. 100124-100124
Open Access
Non-invasive Assessment of Coronary Artery Disease: The Role of AI in the Current Status and Future Directions
Francis Ezekwueme, Oluwaremilekun Tolu-Akinnawo, Zachary Smith, et al.
Cureus (2025)
Open Access
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
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
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: 3
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
AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study
Jirong Yi, Anna M. Michałowska, Aakash Shanbhag, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Research Advances and Applications of Artificial Intelligence in Cardiac CT
Linqi Zou, Ke Xu, Yining Wang
Meta-Radiology (2024) Vol. 2, Iss. 4, pp. 100114-100114
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
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
Facilitating heart disease prediction using deep learning models founded on routinely accessible health data
Runjie Zou, Aimin Li, Dekun Chen, et al.
Research Square (Research Square) (2024)
Closed Access
Association of Cardiovascular Risk Factors and Coronary Calcium Burden with Epicardial Adipose Tissue Volume Obtained from PET–CT Imaging in Oncological Patients
Carmela Nappi, Andrea Ponsiglione, Carlo Vallone, et al.
Journal of Cardiovascular Development and Disease (2024) Vol. 11, Iss. 10, pp. 331-331
Open Access
Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography
Maike Theis, Laura Garajová, Babak Salam, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access
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