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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:

A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study
Ankush D. Jamthikar, Deep Gupta, Amer M. Johri, et al.
Computers in Biology and Medicine (2021) Vol. 140, pp. 105102-105102
Closed Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment
Narendra N. Khanna, Mahesh Maindarkar, Vijay Viswanathan, et al.
Healthcare (2022) Vol. 10, Iss. 12, pp. 2493-2493
Open Access | Times Cited: 162
Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review
Manasvi Singh, Ashish Kumar, Narendra N. Khanna, et al.
EClinicalMedicine (2024) Vol. 73, pp. 102660-102660
Open Access | Times Cited: 28
A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review
Jasjit S. Suri, Mrinalini Bhagawati, Sudip Paul, et al.
Diagnostics (2022) Vol. 12, Iss. 3, pp. 722-722
Open Access | Times Cited: 43
Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization
Amer M. Johri, Krishna V. Singh, Laura E. Mantella, et al.
Computers in Biology and Medicine (2022) Vol. 150, pp. 106018-106018
Closed Access | Times Cited: 34
Artificial intelligence bias in medical system designs: a systematic review
Ashish Kumar, Vivekanand Aelgani, Rubeena Vohra, et al.
Multimedia Tools and Applications (2023) Vol. 83, Iss. 6, pp. 18005-18057
Open Access | Times Cited: 21
Machine Learning and the Conundrum of Stroke Risk Prediction
Yaacoub Chahine, Matthew J Magoon, Bahetihazi Maidu, et al.
Arrhythmia & Electrophysiology Review (2023) Vol. 12
Open Access | Times Cited: 20
Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study
Narendra N. Khanna, Mahesh Maindarkar, Vijay Viswanathan, et al.
Journal of Clinical Medicine (2022) Vol. 11, Iss. 22, pp. 6844-6844
Open Access | Times Cited: 26
NeoAI 1.0: Machine learning-based paradigm for prediction of neonatal and infant risk of death
Jagjit S. Teji, Suneet Jain, Suneet Gupta, et al.
Computers in Biology and Medicine (2022) Vol. 147, pp. 105639-105639
Closed Access | Times Cited: 25
Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review
Mustafa Al-Maini, Mahesh Maindarkar, George D. Kitas, et al.
Rheumatology International (2023) Vol. 43, Iss. 11, pp. 1965-1982
Closed Access | Times Cited: 16
Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm
Ya‐Fang Chen, Zhen-Jie Chen, You‐Yu Lin, et al.
Frontiers in Cardiovascular Medicine (2023) Vol. 10
Open Access | Times Cited: 11
Artificial intelligence-based cardiovascular/stroke risk stratification in women affected by autoimmune disorders: a narrative survey
Ekta Tiwari, Deepti D. Shrimankar, Mahesh Maindarkar, et al.
Rheumatology International (2025) Vol. 45, Iss. 1
Closed Access
Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation
Juan Lü, R. Hutchens, Joseph Hung, et al.
Computers in Biology and Medicine (2022) Vol. 150, pp. 106126-106126
Open Access | Times Cited: 17
Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review
Narendra N Khanna, Manasvi Singh, Mahesh Maindarkar, et al.
Journal of Korean Medical Science (2023) Vol. 38, Iss. 46
Open Access | Times Cited: 10
From algorithms to clinical outcomes: how artificial intelligence shapes metaclinical medicine
Panos Vardas, Charalambos Vlachopoulos
Hellenic Journal of Cardiology (2025) Vol. 81, pp. 1-3
Open Access
Review on the Challenges and Future Directions of Deep Learning-Based Techniques for Advance Prediction of Cardiac Attack
Shrawan Kumar, Bharti Thakur
Communications in computer and information science (2025), pp. 172-189
Closed Access
Attention-based hybrid deep learning models and its scientific validation for cardiovascular disease risk stratification
Mrinalini Bhagawati, Siddharth Gupta, Sudip Paul, et al.
Biomedical Signal Processing and Control (2025) Vol. 108, pp. 107824-107824
Closed Access
Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look
Vandana Kumari, Naresh Kumar, Sampath Kumar K, et al.
Journal of Cardiovascular Development and Disease (2023) Vol. 10, Iss. 12, pp. 485-485
Open Access | Times Cited: 8
A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool
Luca Saba, Mahesh Maindarkar, Narendra N. Khanna, et al.
Frontiers in Bioscience-Landmark (2023) Vol. 28, Iss. 10, pp. 248-248
Open Access | Times Cited: 7
Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome
Xiaolu Xu, Zitong Qi, Xiumei Han, et al.
Computers in Biology and Medicine (2024) Vol. 170, pp. 107992-107992
Closed Access | Times Cited: 2
A multi-label learning prediction model for heart failure in patients with atrial fibrillation based on expert knowledge of disease duration
Youhe Huang, Rongfeng Zhang, Hongru Li, et al.
Applied Intelligence (2023) Vol. 53, Iss. 17, pp. 20047-20058
Closed Access | Times Cited: 6
A Big Data-Driven Risk Assessment Method Using Machine Learning for Supply Chains in Airport Economic Promotion Areas
Zhijun Ma, Xiaobei Yang, Ruili Miao
Journal of Circuits Systems and Computers (2023) Vol. 32, Iss. 10
Closed Access | Times Cited: 4
Personalized Medicine for Cardiovascular Disease Risk in Artificial Intelligence Framework
Manasvi Singh, Ashish Kumar, Narendra N. Khanna, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 4
The Comparative Experimental Study of Rehabilitation Program Decision for Spinal Cord Injury Based on Electronic Medical Records
Botao Qie, Xin Guo, Wei Chen, et al.
Heliyon (2024) Vol. 10, Iss. 16, pp. e36121-e36121
Open Access | Times Cited: 1
Applications of Artificial Intelligence in Vascular Diseases
Kosmas I. Paraskevas, Luca Saba, Jasjit S. Suri
Angiology (2022) Vol. 73, Iss. 7, pp. 597-598
Closed Access | Times Cited: 5
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