Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Machine Learning-Derived Echocardiographic Phenotypes Predict Heart Failure Incidence in Asymptomatic Individuals
Masatake Kobayashi, Olivier Huttin, Martin Magnusson, et al.
JACC. Cardiovascular imaging (2021) Vol. 15, Iss. 2, pp. 193-208
Open Access | Times Cited: 64

Showing 26-50 of 64 citing articles:

Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity
Jicai Deng, Chenxing Zhou, Fei Xiao, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
The Impact of Artificial Intelligence on Cardiovascular Disease Diagnosis: A Review
Ifra Chaudhary, Hassan Anwar
(2024) Vol. 17, Iss. 11, pp. 8-13
Open Access | Times Cited: 3
Advancing Myocardial Tissue Analysis Using Echocardiography
Partho P. Sengupta, Y. Chandrashekhar
JACC. Cardiovascular imaging (2024) Vol. 17, Iss. 2, pp. 228-231
Closed Access | Times Cited: 2
Proteomic biomarkers and pathway analysis for progression to heart failure in three epidemiological representative cohorts
Anna Dieden, Nicolas Girerd, Filip Ottosson, et al.
European Journal of Heart Failure (2024)
Open Access | Times Cited: 2
Expert proposal to characterize cardiac diseases with normal or preserved left ventricular ejection fraction and symptoms of heart failure by comprehensive echocardiography
Andreas Hagendorff, Andreas Helfen, Roland R. Brandt, et al.
Clinical Research in Cardiology (2022) Vol. 112, Iss. 1, pp. 1-38
Open Access | Times Cited: 11
Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics
Chenxing Zhou, Shengsheng Huang, Tuo Liang, et al.
Frontiers in Surgery (2022) Vol. 9
Open Access | Times Cited: 9
A machine learning‐derived echocardiographic algorithm identifies people at risk of heart failure with distinct cardiac structure, function, and response to spironolactone: Findings from the HOMAGE trial
Masatake Kobayashi, Olivier Huttin, João Pedro Ferreira, et al.
European Journal of Heart Failure (2023) Vol. 25, Iss. 8, pp. 1284-1289
Open Access | Times Cited: 5
Proteomic Pathways across Ejection Fraction Spectrum in Heart Failure: an EXSCEL Substudy
Anthony Peters, Maggie Nguyen, Jennifer B. Green, et al.
medRxiv (Cold Spring Harbor Laboratory) (2023)
Open Access | Times Cited: 4
From Conventional Deep Learning to GPT
Partho P. Sengupta, Y. Chandrashekhar
JACC. Cardiovascular imaging (2023) Vol. 16, Iss. 8, pp. 1129-1131
Open Access | Times Cited: 4
Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning
Yuanlin Yao, Shaofeng Wu, Chong Liu, et al.
Annals of Medicine (2023) Vol. 55, Iss. 2
Open Access | Times Cited: 4
Sex Differences in Circulating Biomarkers of Heart Failure
Roopa A. Rao, Anju Bhardwaj, Mrudula Munagala, et al.
Current Heart Failure Reports (2023) Vol. 21, Iss. 1, pp. 11-21
Closed Access | Times Cited: 4
Enhancing machine learning-based survival prediction models for patients with cardiovascular diseases
Tripti Rastogi, Nicolas Girerd
International Journal of Cardiology (2024) Vol. 410, pp. 132195-132195
Closed Access | Times Cited: 1
AI for Cardiac Function Assessment
Partho P. Sengupta, Y. Chandrashekhar
JACC. Cardiovascular imaging (2024) Vol. 17, Iss. 7, pp. 843-845
Closed Access | Times Cited: 1
Sex-based Differences in Heart Failure Biomarkers
Ainhoa Robles Mezcua, Nelsa González Aguado, Antonia Pilar Martin de la Rosa, et al.
Current Heart Failure Reports (2024) Vol. 21, Iss. 4, pp. 379-388
Closed Access | Times Cited: 1
Association of ventricular–arterial coupling with biomarkers involved in heart failure pathophysiology – the STANISLAS cohort
Hannes Holm, Martin Magnusson, Amra Jujić, et al.
European Journal of Heart Failure (2024)
Open Access | Times Cited: 1
Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures
B Ferreira, Masatake Kobayashi, Rita Quelhas Costa, et al.
Scandinavian Journal of Rheumatology (2023) Vol. 52, Iss. 6, pp. 619-626
Open Access | Times Cited: 3
Diagnostic role of echocardiography for patients with heart failure symptoms and preserved left ventricular ejection fraction
Andreas Hagendorff, Stephan Stöbe, Joscha Kandels, et al.
Herz (2022) Vol. 47, Iss. 4, pp. 293-300
Open Access | Times Cited: 4
Cardiovascular Imaging in Cardio-Oncology
John Alan Gambril, Aaron Chum, Akash Goyal, et al.
Heart Failure Clinics (2022) Vol. 18, Iss. 3, pp. 455-478
Open Access | Times Cited: 4
Identification of Clinical Heterogeneity and Construction of Prediction Models for Novel Subtypes in Patients with Abdominal Aortic Aneurysm: An Unsupervised Machine Learning Study
Lin Zhang, Jingpeng Wei, Jindou Wei, et al.
Annals of Vascular Surgery (2023) Vol. 98, pp. 75-86
Open Access | Times Cited: 2
Optimizing the Use of Artificial Intelligence in Cardiology in 2024
Stephen G. Ellis, Michael W. Kattan
КАРДИОЛОГИЯ УЗБЕКИСТАНА (2024) Vol. 17, Iss. 14, pp. 1717-1718
Closed Access
Artificial intelligence-driven multiomics predictive model for abdominal aortic aneurysm subtypes to identify heterogeneous immune cell infiltration and predict disease progression
Lin Zhang, Yang Han, Chenxing Zhou, et al.
International Immunopharmacology (2024) Vol. 138, pp. 112608-112608
Open Access
Machine learning in the prevention of heart failure
Arsalan Hamid, Matthew W. Segar, Biykem Bozkurt, et al.
Heart Failure Reviews (2024)
Closed Access
The new era of evidence-based echocardiographic algorithms using artificial intelligence
Nicolas Girerd, Masatake Kobayashi
International Journal of Cardiology (2023) Vol. 380, pp. 35-36
Closed Access | Times Cited: 1
A new evidence-based echocardiographic approach to predict cardiovascular events and myocardial fibrosis in mitral valve prolapse: The STAMP algorithm
Olivier Huttin, Thierry Le Tourneau, Laure Filippetti, et al.
Archives of cardiovascular diseases (2024) Vol. 117, Iss. 3, pp. 173-176
Closed Access
Phenotypes of Vascular Aging
Pedro Cunha, Peter M. Nilsson, Pierre Boutouyrie, et al.
Elsevier eBooks (2024), pp. 371-378
Closed Access

Scroll to top