2024
Artificial 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, ehae415. PMID: 38976371, DOI: 10.1093/eurheartj/ehae415.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsEfficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning
Holste G, Oikonomou E, Mortazavi B, Wang Z, Khera R. Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning. Communications Medicine 2024, 4: 133. PMID: 38971887, PMCID: PMC11227494, DOI: 10.1038/s43856-024-00538-3.Peer-Reviewed Original ResearchSelf-supervised learningTransfer learningTraining dataEchocardiogram videosPortion of labelled dataStandard transfer learning approachContrastive self-supervised learningSelf-supervised learning approachLearning approachImage recognition tasksState-of-the-artContrastive learning approachFine-tuningTransfer learning approachMedical image diagnosisCardiac disease diagnosisContrastive learningVideo framesLabeled datasetLabeled dataExpert labelsClassification performanceMedical imagesRecognition taskVideoTransforming 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 Original ResearchA 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 ResearchCardiac 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 structureReal-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study
Oikonomou E, Aminorroaya A, Dhingra L, Partridge C, Velazquez E, Desai N, Krumholz H, Miller E, Khera R. Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study. European Heart Journal - Digital Health 2024, 5: 303-313. PMID: 38774380, PMCID: PMC11104476, DOI: 10.1093/ehjdh/ztae023.Peer-Reviewed Original ResearchRisk of acute myocardial infarctionAssociated with lower oddsHospital health systemCoronary artery diseaseCardiac testingRisk of adverse outcomesUK BiobankHealth systemProvider-drivenLower oddsAssociated with better outcomesAcute myocardial infarctionBlack raceStable chest painFemale sexReal world evaluationDiabetes historyMulticohort studyFunction testsSuspected coronary artery diseaseYounger ageRisk profileAdverse outcomesMultinational cohortPost hoc analysisTRENDS IN KNOWLEDGE OF RISK FACTOR TARGETS AMONG PATIENTS WITH DIABETES MELLITUS IN THE UNITED STATES: A NATIONALLY REPRESENTATIVE STUDY
Bansal B, Aminorroaya A, Dhingra L, Oikonomou E, Khera R. TRENDS IN KNOWLEDGE OF RISK FACTOR TARGETS AMONG PATIENTS WITH DIABETES MELLITUS IN THE UNITED STATES: A NATIONALLY REPRESENTATIVE STUDY. Journal Of The American College Of Cardiology 2024, 83: 1882. DOI: 10.1016/s0735-1097(24)03872-5.Peer-Reviewed Original ResearchHEART FAILURE RISK PREDICTION USING ARTIFICIAL INTELLIGENCE ON ECG PHOTOS IN LARGE CONTEMPORARY COHORT
Dhingra L, Sangha V, Aminorroaya A, Camargos A, Oikonomou E, Khera R. HEART FAILURE RISK PREDICTION USING ARTIFICIAL INTELLIGENCE ON ECG PHOTOS IN LARGE CONTEMPORARY COHORT. Journal Of The American College Of Cardiology 2024, 83: 277. DOI: 10.1016/s0735-1097(24)02267-8.Peer-Reviewed Original ResearchMULTINATIONAL REAL-WORLD EVALUATION OF A MACHINE LEARNING-DERIVED TOOL FOR ANATOMICAL VERSUS FUNCTIONAL TESTING IN SUSPECTED CORONARY ARTERY DISEASE
Oikonomou E, Aminorroaya A, Dhingra L, Khera R. MULTINATIONAL REAL-WORLD EVALUATION OF A MACHINE LEARNING-DERIVED TOOL FOR ANATOMICAL VERSUS FUNCTIONAL TESTING IN SUSPECTED CORONARY ARTERY DISEASE. Journal Of The American College Of Cardiology 2024, 83: 1357. DOI: 10.1016/s0735-1097(24)03347-3.Peer-Reviewed Original ResearchEVIDENCE FROM RANDOMIZED CONTROLLED TRIAL TO REAL-WORLD PATIENTS USING ELECTRONIC HEALTH RECORD-ADAPTED DIGITAL TWINS: A NOVEL APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE
Thangaraj P, Shankar S, Oikonomou E, Khera R. EVIDENCE FROM RANDOMIZED CONTROLLED TRIAL TO REAL-WORLD PATIENTS USING ELECTRONIC HEALTH RECORD-ADAPTED DIGITAL TWINS: A NOVEL APPLICATION OF GENERATIVE ARTIFICIAL INTELLIGENCE. Journal Of The American College Of Cardiology 2024, 83: 2340. DOI: 10.1016/s0735-1097(24)04330-4.Peer-Reviewed Original ResearchCROSS-MODAL VALIDATION OF AN ARTIFICIAL INTELLIGENCE VIDEO-BASED APPROACH FOR THE AUTOMATED RISK STRATIFICATION OF AORTIC STENOSIS
Oikonomou E, Holste G, Nadkarni G, Wang Z, Khera R. CROSS-MODAL VALIDATION OF AN ARTIFICIAL INTELLIGENCE VIDEO-BASED APPROACH FOR THE AUTOMATED RISK STRATIFICATION OF AORTIC STENOSIS. Journal Of The American College Of Cardiology 2024, 83: 1418. DOI: 10.1016/s0735-1097(24)03408-9.Peer-Reviewed Original ResearchA WEARABLE-ADAPTED ARTIFICIAL INTELLIGENCE ALGORITHM FOR HEART FAILURE PREDICTION FROM SINGLE-LEAD ELECTROCARDIOGRAMS IN A LARGE NATIONWIDE COHORT STUDY
Dhingra L, Aminorroaya A, Oikonomou E, Sangha V, Khunte A, Khera R. A WEARABLE-ADAPTED ARTIFICIAL INTELLIGENCE ALGORITHM FOR HEART FAILURE PREDICTION FROM SINGLE-LEAD ELECTROCARDIOGRAMS IN A LARGE NATIONWIDE COHORT STUDY. Journal Of The American College Of Cardiology 2024, 83: 2341. DOI: 10.1016/s0735-1097(24)04331-6.Peer-Reviewed Original ResearchLeveraging the Full Potential of Wearable Devices in Cardiomyopathies
Oikonomou E, Khera R. Leveraging the Full Potential of Wearable Devices in Cardiomyopathies. Journal Of Cardiac Failure 2024, 30: 964-966. PMID: 38452997, DOI: 10.1016/j.cardfail.2024.02.011.Peer-Reviewed Original ResearchBiometric contrastive learning for data-efficient deep learning from electrocardiographic images
Sangha V, Khunte A, Holste G, Mortazavi B, Wang Z, Oikonomou E, Khera R. Biometric contrastive learning for data-efficient deep learning from electrocardiographic images. Journal Of The American Medical Informatics Association 2024, 31: 855-865. PMID: 38269618, PMCID: PMC10990541, DOI: 10.1093/jamia/ocae002.Peer-Reviewed Original ResearchLabeled training dataContrastive learningECG imagesLabeled dataTraining dataDeep learningProportions of labeled dataArtificial intelligenceSelf-supervised contrastive learningTraditional supervised learningConvolutional neural networkHeld-out test setSupervised learningPretraining strategyBiometric signatureImageNet initializationPretraining approachNeural networkImageNetAI modelsImage objectsTest setLearningDetect atrial fibrillationEquivalent performance
2023
An 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 ResearchPredicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography
Oikonomou E, Holste G, Mcnamara R, Velazquez E, Nadkarni G, Ouyang D, Krumholz H, Wang Z, Khera R. Predicting aortic stenosis progression using a video-based deep learning model of aortic stenosis built for single-view two-dimensional echocardiography. European Heart Journal 2023, 44: ehad655.040. DOI: 10.1093/eurheartj/ehad655.040.Peer-Reviewed Original ResearchLeft ventricular ejection fractionSevere aortic stenosisAortic stenosisAS progressionAV VmaxTransthoracic echocardiographyYale New Haven Health SystemBaseline left ventricular ejection fractionAortic stenosis progressionModerate aortic stenosisRetrospective cohort studyVentricular ejection fractionTwo-dimensional echocardiographyMean rateModerate ASAS severityCohort studyEjection fractionPatient sexStenosis progressionTTE studiesEligible participantsSerial monitoringSpecialized centersTimely diagnosisMachine learning in precision diabetes care and cardiovascular risk prediction
Oikonomou E, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovascular Diabetology 2023, 22: 259. PMID: 37749579, PMCID: PMC10521578, DOI: 10.1186/s12933-023-01985-3.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsArtificial intelligence solutionsArtificial intelligence productsData-driven methodIntelligence solutionsArtificial intelligenceMachine learningPersonalized solutionsIntelligence productsBias mitigationMachineKey issuesPredictive modelSuch modelsSuccessful applicationRisk predictionParadigm shiftIntelligenceKey propertiesApplicationsLearningPersonalized careFrameworkSolutionCurrent regulatory frameworkHealthcareSevere 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 ResearchUse of Smart Devices to Track Cardiovascular Health Goals in the United States
Aminorroaya A, Dhingra L, Nargesi A, Oikonomou E, Krumholz H, Khera R. Use of Smart Devices to Track Cardiovascular Health Goals in the United States. JACC Advances 2023, 2: 100544. PMID: 38094515, PMCID: PMC10718569, DOI: 10.1016/j.jacadv.2023.100544.Peer-Reviewed Original ResearchHealth goalsRisk of cardiovascular diseaseCardiovascular risk factorsNationally representative Health Information National Trends SurveyHealth Information National Trends SurveyU.S. adultsCardiovascular diseaseNational Trends SurveyRisk factors of hypertensionDigital health interventionsCardiovascular health goalsHealth-related goalsRisk of CVDFactors of hypertensionU.S. adult populationCardiovascular risk managementHigher educational attainmentLow-income individualsSmart devicesTrends SurveyImprove careHealth interventionsNational estimatesRisk factorsSurvey participantsDetection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
Khunte A, Sangha V, Oikonomou E, Dhingra L, Aminorroaya A, Mortazavi B, Coppi A, Brandt C, Krumholz H, Khera R. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. Npj Digital Medicine 2023, 6: 124. PMID: 37433874, PMCID: PMC10336107, DOI: 10.1038/s41746-023-00869-w.Peer-Reviewed Original ResearchArtificial intelligenceRandom Gaussian noiseNoisy electrocardiogramGaussian noiseElectrocardiogram (ECGWearable devicesSingle-lead electrocardiogramPortable devicesSNRWearableNoiseDevice noiseRepositoryAI-based screeningIntelligenceDetectionDevicesNoise sourcesVentricular systolic dysfunctionModelElectrocardiogramSingle-lead electrocardiographyTrainingUse of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020
Dhingra L, Aminorroaya A, Oikonomou E, Nargesi A, Wilson F, Krumholz H, Khera R. Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020. JAMA Network Open 2023, 6: e2316634. PMID: 37285157, PMCID: PMC10248745, DOI: 10.1001/jamanetworkopen.2023.16634.Peer-Reviewed Original ResearchConceptsHealth Information National Trends SurveyUS adultsExacerbate disparitiesWearable device usersCardiovascular diseaseCardiovascular healthPopulation-based cross-sectional studySelf-reported cardiovascular diseaseCardiovascular disease risk factorsNational Trends SurveyOverall US adult populationCardiovascular risk factor profileSelf-reported accessAssociated with lower useUse of wearable devicesImprove cardiovascular healthLower household incomeLower educational attainmentUS adult populationRisk factor profileNationally representative sampleCross-sectional studyProportion of adultsTrends SurveyWearable device data