Evangelos K. Oikonomou, MD, DPhil
Clinical FellowDownloadHi-Res Photo
Cards
Education
DPhil
University of Oxford (2020)
University of Oxford (2020)
MD
University of Athens School of Health Sciences (2015)
University of Athens School of Health Sciences (2015)
Training
Clinical fellow
Yale School of Medicine (2025)
Yale School of Medicine (2025)
Post-doctoral fellow
Yale School of Medicine (2025)
Yale School of Medicine (2025)
CarDS lab
Education
DPhil
University of Oxford (2020)
University of Oxford (2020)
MD
University of Athens School of Health Sciences (2015)
University of Athens School of Health Sciences (2015)
Training
Clinical fellow
Yale School of Medicine (2025)
Yale School of Medicine (2025)
Post-doctoral fellow
Yale School of Medicine (2025)
Yale School of Medicine (2025)
CarDS lab
Education
DPhil
University of Oxford (2020)
University of Oxford (2020)
MD
University of Athens School of Health Sciences (2015)
University of Athens School of Health Sciences (2015)
Training
Clinical fellow
Yale School of Medicine (2025)
Yale School of Medicine (2025)
Post-doctoral fellow
Yale School of Medicine (2025)
Yale School of Medicine (2025)
CarDS lab
About
Titles
Clinical Fellow
Biography
Evangelos (Evan) Oikonomou is a clinical fellow in cardiovascular medicine, a post-doctoral research fellow in the Cardiovascular Data Science (CarDS) lab, and a member of the ABIM Physician-Scientist Research Pathway at Yale. His work focuses on the intersection of applied computer vision and statistical machine learning, with a specific focus on developing tools for the improved phenotyping of cardiovascular disease using scalable approaches that can be deployed at minimal cost using existing care pathways. He graduated as valedictorian of his class from the University of Athens Medical School in Greece, before pursuing a Ph.D. (D.Phil.) degree at the University of Oxford, where he was recognized with the Radcliffe Department of Medicine Graduate Prize for his scientific work. In 2019 he joined the Physician-Scientist Training Program at the Yale School of Medicine, and he has since completed his internal medicine residency and his core clinical fellowship in cardiology.
He is a recipient of an F32 NRSA fellowship award from NHLBI (National Institutes of Health), and his work has been recognized through numerous Young Investigator Awards sponsored by the American Heart Association, American College of Cardiology, Northwestern Cardiovascular Young Investigator Forum, the European Society of Cardiology and European Association of Preventive Cardiology.
He has led a broad portfolio in applied artificial intelligence in cardiovascular and cardiometabolic medicine. First, he has defined and translated a key interplay between the perivascular adipose tissue and vascular inflammation in humans into a clinically actionable algorithmic tool that can refine cardiovascular risk on routine cardiac CT scans. Second, he has developed and validated deep learning algorithms for the efficient diagnosis of common and rare cardiomyopathies specifically adapted for point-of-care echocardiography. Finally, he has led an extensive body of work on defining treatment effect heterogeneity across clinical trials, with direct implications for evidence translation and the design of new adaptive trials with data-driven predictive enrichment.
His work has been published in several peer-reviewed journals, including the Lancet, Lancet Digital Health, European Heart Journal, JACC, Circulation, JAMA Cardiology, Diabetes Care.
A detailed list of Dr. Oikonomou's publications can be found here: https://www.ncbi.nlm.nih.gov/myncbi/evangelos.oikonomou.1/bibliography/public/ and https://scholar.google.com/citations?user=GgJv1SMAAAAJ&hl=en
Departments & Organizations
- ABIM Physician-Scientist Research Pathway
- Cardiovascular Data Science Lab (CarDS)
- Internal Medicine
Education & Training
- Clinical fellow
- Yale School of Medicine (2025)
- Post-doctoral fellow
- Yale School of Medicine (2025)
- Resident
- Yale School of Medicine (2021)
- Intern
- Yale School of Medicine (2020)
- DPhil
- University of Oxford (2020)
- MD
- University of Athens School of Health Sciences (2015)
Board Certifications
Internal Medicine
- Certification Organization
- ABIM
- Original Certification Date
- 2022
Research
Overview
Medical Research Interests
Adaptive Clinical Trial; Adipose Tissue; Artificial Intelligence; Cardiovascular Diseases; Computed Tomography Angiography; Echocardiography; Machine Learning; Multimodal Imaging
ORCID
0000-0003-4362-0720- View Lab Website
CarDS lab
Research at a Glance
Research Interests
Research topics Evangelos K. Oikonomou is interested in exploring.
Cardiovascular Diseases
Adipose Tissue
Computed Tomography Angiography
Artificial Intelligence
Machine Learning
Echocardiography
Publications
Featured Publications
A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression
Oikonomou E, Holste G, Yuan N, Coppi A, McNamara R, Haynes N, Vora A, Velazquez E, Li F, Menon V, Kapadia S, Gill T, Nadkarni G, Krumholz H, Wang Z, Ouyang D, Khera R. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiology 2024, 9: 534-544. PMID: 38581644, PMCID: PMC10999005, DOI: 10.1001/jamacardio.2024.0595.Peer-Reviewed Original ResearchCitationsAltmetricConceptsCardiac magnetic resonanceAortic valve replacementCardiac magnetic resonance imagingAV VmaxSevere ASAortic stenosisCohort studyPeak aortic valve velocityCohort study of patientsAortic valve velocityCohort of patientsTraditional cardiovascular risk factorsAssociated with faster progressionStudy of patientsCedars-Sinai Medical CenterAssociated with AS developmentCardiovascular risk factorsCardiovascular imaging modalitiesIndependent of ageModerate ASEjection fractionEchocardiographic studiesValve replacementRisk stratificationCardiac structureArtificial intelligence-enhanced patient evaluation: bridging art and science
Oikonomou E, Khera R. Artificial intelligence-enhanced patient evaluation: bridging art and science. European Heart Journal 2024, 45: 3204-3218. PMID: 38976371, PMCID: PMC11400875, DOI: 10.1093/eurheartj/ehae415.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCitationsAltmetricTransforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice JACC State-of-the-Art Review
Khera R, Oikonomou E, Nadkarni G, Morley J, Wiens J, Butte A, Topol E. Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice JACC State-of-the-Art Review. Journal Of The American College Of Cardiology 2024, 84: 97-114. PMID: 38925729, DOI: 10.1016/j.jacc.2024.05.003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCitationsAltmetricMeSH Keywords and ConceptsArtificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study.
Oikonomou EK, Vaid A, Holste G, Coppi A, McNamara RL, Baloescu C, Krumholz HM, Wang Z, Apakama DJ, Nadkarni GN, Khera R. Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study. MedRxiv 2024 PMID: 38559021, DOI: 10.1101/2024.03.10.24304044.Peer-Reviewed Original ResearchAn explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials
Oikonomou E, Thangaraj P, Bhatt D, Ross J, Young L, Krumholz H, Suchard M, Khera R. An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials. Npj Digital Medicine 2023, 6: 217. PMID: 38001154, PMCID: PMC10673945, DOI: 10.1038/s41746-023-00963-z.Peer-Reviewed Original ResearchCitationsAltmetricSevere aortic stenosis detection by deep learning applied to echocardiography
Holste G, Oikonomou E, Mortazavi B, Coppi A, Faridi K, Miller E, Forrest J, McNamara R, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz H, Wang Z, Khera R. Severe aortic stenosis detection by deep learning applied to echocardiography. European Heart Journal 2023, 44: 4592-4604. PMID: 37611002, PMCID: PMC11004929, DOI: 10.1093/eurheartj/ehad456.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsIndividualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials
Oikonomou EK, Spatz ES, Suchard MA, Khera R. Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials. The Lancet Digital Health 2022, 4: e796-e805. PMID: 36307193, PMCID: PMC9768739, DOI: 10.1016/s2589-7500(22)00170-4.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSystolic blood pressure controlBlood pressure controlIntensive systolic blood pressure controlType 2 diabetesPressure controlCardiovascular benefitsClinical trialsMajor adverse cardiovascular eventsFirst major adverse cardiovascular eventLarge randomised clinical trialsACCORD-BP trialAdverse cardiovascular eventsRandomised clinical trialsSystolic blood pressureCox regression analysisTreatment effectsHazard ratio estimatesACCORD-BPBP trialCardiovascular eventsBlood pressurePrimary outcomeStandard treatmentBaseline variablesIndex patientsA phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST)
Oikonomou EK, Van Dijk D, Parise H, Suchard MA, de Lemos J, Antoniades C, Velazquez EJ, Miller EJ, Khera R. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). European Heart Journal 2021, 42: 2536-2548. PMID: 33881513, PMCID: PMC8488385, DOI: 10.1093/eurheartj/ehab223.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsStable chest painChest painPrimary endpointMajor adverse cardiovascular eventsNon-fatal myocardial infarctionAdverse cardiovascular eventsStudy's primary endpointCoronary artery diseaseClinical trial populationsCox regression modelParticipant-level dataSCOT-HEARTCardiovascular eventsCause mortalityHazard ratioPatients 5Artery diseaseFunctional testingPROMISE trialTrial populationMyocardial infarctionLower incidenceStudy populationPainCollected variablesPerivascular Fat Attenuation Index Stratifies Cardiac Risk Associated With High-Risk Plaques in the CRISP-CT Study
Oikonomou EK, Desai MY, Marwan M, Kotanidis CP, Antonopoulos AS, Schottlander D, Channon KM, Neubauer S, Achenbach S, Antoniades C. Perivascular Fat Attenuation Index Stratifies Cardiac Risk Associated With High-Risk Plaques in the CRISP-CT Study. Journal Of The American College Of Cardiology 2020, 76: 755-757. PMID: 32762910, DOI: 10.1016/j.jacc.2020.05.078.Peer-Reviewed Original ResearchCitationsAltmetricA novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography
Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, Thomas KE, Thomas S, Akoumianakis I, Fan LM, Kesavan S, Herdman L, Alashi A, Centeno EH, Lyasheva M, Griffin BP, Flamm SD, Shirodaria C, Sabharwal N, Kelion A, Dweck MR, Van Beek EJR, Deanfield J, Hopewell JC, Neubauer S, Channon KM, Achenbach S, Newby DE, Antoniades C. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. European Heart Journal 2019, 40: 3529-3543. PMID: 31504423, PMCID: PMC6855141, DOI: 10.1093/eurheartj/ehz592.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsPerivascular adipose tissueFat attenuation indexCoronary CT angiographyCardiac risk predictionCoronary perivascular adipose tissueMajor adverse cardiac eventsCT angiographyRisk predictionHigh-risk plaque featuresPerivascular fat attenuation indexRadiomic featuresAdverse cardiac eventsConsecutive eligible participantsSCOT-HEART trialTraditional risk stratificationCoronary artery diseaseCoronary calcium scoreStandard coronary CT angiographyAcute myocardial infarctionCoronary inflammationCardiac eventsArtery diseaseCalcium scoreCardiac surgeryMACE prediction
Academic Achievements & Community Involvement
activity European Heart Journal
Journal ServiceAssociate EditorDetails2024 - Presentactivity AI-Enabled Diagnostic and Prognostic Biomarkers in Echocardiography
Oral PresentationYale Cardiovascular Imaging SymposiumDetails12/04/2024 - PresentNew Haven, CT, United Statesactivity Scalable AI biomarkers for echocardiography to enable prediction, diagnosis, and prognostication: A JAMA session
LectureAmerican Heart Association Scientific SessionsDetails11/17/2024 - PresentChicago, IL, United Statesactivity AI applied to ECG images for the risk stratification of cancer therapeutics-related cardiac dysfunction
Oral PresentationSt Francis Heart Center OPCI-LiveDetails11/04/2024 - PresentRemoteactivity AI-guided screening of cardiomyopathies using POCUS
LectureMount Sinai Emergency Medicine Hybrid Artificial Intelligence ConferenceDetails10/30/2024 - PresentNew York, NY, United States
News & Links
News
- September 04, 2024
Personalizing Clinical Trial Results: The Future of Evidence Generation
- August 06, 2024
Yale Researchers at European Society of Cardiology Conference 2024
- June 27, 2024
What to Know about Lipoprotein(a)
- June 12, 2024Source: Yale New Haven Health
Yale New Haven Health and Yale University celebrate Innovation Awards
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Yale School of Medicine
195 Church St, 6th floor
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