Showing 23 publications by Veer Sangha
Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.
Oikonomou EK, Sangha V, Dhingra LS, Aminorroaya A, Coppi A et al. (2025), Circulation. Cardiovascular quality and outcomes, 18(1), e011504
BibTeX
@article{artificialintel-2025/1,
title={Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.},
author={Oikonomou EK, Sangha V, Dhingra LS, Aminorroaya A, Coppi A et al.},
journal={Circulation. Cardiovascular quality and outcomes},
volume={18},
pages={e011504},
year = "2025"
}
Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study
Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Asselbergs FW et al. (2025), European Heart Journal
BibTeX
@article{heartfailureris-2025/1,
title={Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study},
author={Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Asselbergs FW et al.},
journal={European Heart Journal},
number={ehae914},
publisher={Oxford University Press},
year = "2025"
}
An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.
Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Shankar SV et al. (2024)
Artificial Intelligence Applications for Electrocardiography to Define New Digital Biomarkers of Cardiovascular Risk.
Sangha V & Khera R (2024), Circulation. Cardiovascular quality and outcomes, 17(12), e011483
BibTeX
@article{artificialintel-2024/12,
title={Artificial Intelligence Applications for Electrocardiography to Define New Digital Biomarkers of Cardiovascular Risk.},
author={Sangha V & Khera R},
journal={Circulation. Cardiovascular quality and outcomes},
volume={17},
pages={e011483},
year = "2024"
}
Biometric contrastive learning for data-efficient deep learning from electrocardiographic images.
Sangha V, Khunte A, Holste G, Mortazavi BJ, Wang Z et al. (2024), Journal of the American Medical Informatics Association : JAMIA, 31(4), 855-865
BibTeX
@article{biometriccontra-2024/4,
title={Biometric contrastive learning for data-efficient deep learning from electrocardiographic images.},
author={Sangha V, Khunte A, Holste G, Mortazavi BJ, Wang Z et al.},
journal={Journal of the American Medical Informatics Association : JAMIA},
volume={31},
pages={855-865},
year = "2024"
}
A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers.
Dhingra LS, Sangha V, Aminorroaya A, Bryde R, Gaballa A et al. (2024)
Augmenting reality in echocardiography.
Sangha V (2024), Heart (British Cardiac Society), 110(6), 387-388
Biometric contrastive learning for data-efficient deep learning from electrocardiographic images
Sangha V, Khunte A, Holste G, Mortazavi BJ, Wang Z et al. (2024), medRxiv, 31(4), 855-865
Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care
Khunte A, Sangha V, Oikonomou E, Dhingra LS, Aminorroaya A et al. (2024)
Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study
Dhingra L, Aminorroaya A, Sangha V, Camargos AP, Asselbergs F et al. (2024)
Artificial Intelligence Applied to Electrocardiographic Images for Scalable Screening of Transthyretin Amyloid Cardiomyopathy
Sangha V, Oikonomou E & Khera R (2024)
Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.
Sangha V, Nargesi AA, Dhingra LS, Khunte A, Mortazavi BJ et al. (2023), Circulation, 148(9), 765-777
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices.
Khunte A, Sangha V, Oikonomou EK, Dhingra LS, Aminorroaya A et al. (2023), NPJ digital medicine, 6(1), 124
BibTeX
@article{detectionofleft-2023/7,
title={Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices.},
author={Khunte A, Sangha V, Oikonomou EK, Dhingra LS, Aminorroaya A et al.},
journal={NPJ digital medicine},
volume={6},
pages={124},
year = "2023"
}
Biometric Contrastive Learning for Data-Efficient Deep Learning from Electrocardiographic Images
Sangha V, Khunte A, Holste G, Mortazavi B, Wang Z et al. (2023)
Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning
Sangha V, Dhingra LS, Oikonomou E, Aminorroaya A, Sikand N et al. (2023)
Deep Learning-enabled Detection of Aortic Stenosis from Noisy Single Lead Electrocardiograms
Aminorroaya A, Dhingra L, Sangha V, Oikonomou E, Khunte A et al. (2023)
Study Protocol for the Pilot Evaluation for SMartphone-adaptable Artificial Intelligence for PRediction and DeTection of Left Ventricular Systolic Dysfunction (The SMART-LV Pilot Study Protocol)
Dhingra LS, Aminorroaya A, Sangha V, Khunte A, Oikonomou E et al. (2023)
BibTeX
@misc{studyprotocolfo-2023/,
title={Study Protocol for the Pilot Evaluation for SMartphone-adaptable Artificial Intelligence for PRediction and DeTection of Left Ventricular Systolic Dysfunction (The SMART-LV Pilot Study Protocol)},
author={Dhingra LS, Aminorroaya A, Sangha V, Khunte A, Oikonomou E et al.},
year = "2023"
}
County-level variation in cardioprotective antihyperglycemic prescribing among medicare beneficiaries.
Hanna J, Nargesi AA, Essien UR, Sangha V, Lin Z et al. (2022), American journal of preventive cardiology, 11, 100370
BibTeX
@article{countylevelvari-2022/9,
title={County-level variation in cardioprotective antihyperglycemic prescribing among medicare beneficiaries.},
author={Hanna J, Nargesi AA, Essien UR, Sangha V, Lin Z et al.},
journal={American journal of preventive cardiology},
volume={11},
pages={100370},
year = "2022"
}
A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations.
Khera R, Mortazavi BJ, Sangha V, Warner F, Patrick Young H et al. (2022), NPJ digital medicine, 5(1), 27
BibTeX
@article{amulticentereva-2022/3,
title={A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations.},
author={Khera R, Mortazavi BJ, Sangha V, Warner F, Patrick Young H et al.},
journal={NPJ digital medicine},
volume={5},
pages={27},
year = "2022"
}
Automated multilabel diagnosis on electrocardiographic images and signals.
Sangha V, Mortazavi BJ, Haimovich AD, Ribeiro AH, Brandt CA et al. (2022), Nature communications, 13(1), 1583
Detection of Left Ventricular Systolic Dysfunction from Single-Lead Electrocardiography Adapted for Wearable Devices
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A et al. (2022)
Patterns of Prescribing Sodium-Glucose Cotransporter-2 Inhibitors for Medicare Beneficiaries in the United States.
Sangha V, Lipska K, Lin Z, Inzucchi SE, McGuire DK et al. (2021), Circulation. Cardiovascular quality and outcomes, 14(12), e008381
BibTeX
@article{patternsofpresc-2021/12,
title={Patterns of Prescribing Sodium-Glucose Cotransporter-2 Inhibitors for Medicare Beneficiaries in the United States.},
author={Sangha V, Lipska K, Lin Z, Inzucchi SE, McGuire DK et al.},
journal={Circulation. Cardiovascular quality and outcomes},
volume={14},
pages={e008381},
year = "2021"
}
Detection of Left Ventricular Systolic Dysfunction from Electrocardiographic Images
Sangha V, Nargesi AA, Dhingra LS, Khunte A, Mortazavi BJ et al. (0)