Oikonomou, Evangelos K.

A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

Oikonomou, E. K., Williams, M. C., Kotanidis, C. P., Desai, M. Y., Marwan, M., Antonopoulos, A. S., Thomas, K. E., Thomas, S., Akoumianakis, I., Fan, L. M., et al. (2019) · European Heart Journal

Cardiovascular Disease and AdiposityRadiomics and Machine Learning in Medical Imaging

Cited by 534

Machine learning in precision diabetes care and cardiovascular risk prediction

Oikonomou, E. K., & Khera, R. (2023) · Cardiovascular Diabetology

Artificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 190

Use of wearable devices in individuals with or at risk for cardiovascular disease in the US, 2019 to 2020

Dhingra, L. S., Aminorroaya, A., Oikonomou, E. K., Nargesi, A. A., Wilson, F. P., Krumholz, H. M., & Khera, R. (2023) · JAMA Network Open

Mobile Health and mHealth ApplicationsNon-Invasive Vital Sign Monitoring

Cited by 158

Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices

Khunte, A., Sangha, V., Oikonomou, E. K., Dhingra, L. S., Aminorroaya, A., Mortazavi, B. J., Coppi, A., Brandt, C. A., Krumholz, H. M., & Khera, R. (2023) · npj Digital Medicine

ECG Monitoring and AnalysisNon-Invasive Vital Sign Monitoring

Cited by 95

Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms

Dhingra, L. S., Aminorroaya, A., Pedroso, A. F., Khunte, A., Sangha, V., McIntyre, D., Chow, C. K., Asselbergs, F. W., Brant, L. C. C., Barreto, S. M., et al. (2025) · JAMA Cardiology

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 65

Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images

Oikonomou, E. K., Sangha, V., Dhingra, L. S., Aminorroaya, A., Coppi, A., Krumholz, H. M., Baldassarre, L. A., & Khera, R. (2025) · Circulation: Cardiovascular Quality and Outcomes

Chemotherapy-induced cardiotoxicity and mitigationCardiovascular Function and Risk Factors

Cited by 60

Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images

Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Shankar, S. V., Coppi, A., Foppa, M., Brant, L. C. C., Barreto, S. M., Ribeiro, A. L. P., et al. (2025) · Journal of the American College of Cardiology

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 53

Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning

Sangha, V., Dhingra, L. S., Aminorroaya, A., Croon, P. M., Sikand, N. V., Sen, S., Martinez, M. W., Maron, M. S., Krumholz, H. M., Asselbergs, F. W., et al. (2025) · Nature Cardiovascular Research

ECG Monitoring and AnalysisCardiomyopathy and Myosin Studies

Cited by 41

Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning

Sangha, V., Dhingra, L. S., Aminorroaya, A., Croon, P. M., Sikand, N. V., Sen, S., Martinez, M. W., Maron, M. S., Krumholz, H. M., Asselbergs, F. W., et al. (2023) · Nature Cardiovascular Research

ECG Monitoring and AnalysisCardiomyopathy and Myosin Studies

Cited by 38

Artificial intelligence-enhanced patient evaluation: bridging art and science

Oikonomou, E. K., & Khera, R. (2024) · European Heart Journal

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 34

Semaglutide concurrently improves vascular and liver indices in patients with type 2 diabetes and fatty liver disease

Korakas, E., Kountouri, A., Pavlidis, G., Oikonomou, E., Vrentzos, E., Michalopoulou, E., Tsigkou, V., Katogiannis, K., Pliouta, L., Balampanis, K., et al. (2024) · Journal of the Endocrine Society

Diabetes Treatment and ManagementLiver Disease Diagnosis and Treatment

Cited by 18

A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers

Dhingra, L. S., Sangha, V., Aminorroaya, A., Bryde, R., Gaballa, A., Ali, A. H., Mehra, N., Krumholz, H. M., Sen, S., Kramer, C. M., et al. (2025) · The American Journal of Cardiology

Cardiomyopathy and Myosin StudiesECG Monitoring and Analysis

Cited by 18

Tracking the Preclinical Progression of Transthyretin Amyloid Cardiomyopathy Using Artificial Intelligence-Enabled Electrocardiography and Echocardiography

Oikonomou, E. K., Sangha, V., Vasisht Shankar, S., Coppi, A., Krumholz, H. M., Nasir, K., Miller, E. J., Gallegos-Kattan, C., Al-Mallah, M. H., Al-Kindi, S., et al. (2024) · medRxiv

Amyloidosis: Diagnosis, Treatment, OutcomesArtificial Intelligence in Healthcare and Education

Cited by 7

Leveraging the full potential of wearable devices in cardiomyopathies

Oikonomou, E. K., & Khera, R. (2024) · Journal of Cardiac Failure

Non-Invasive Vital Sign MonitoringECG Monitoring and Analysis

Cited by 6