Lovedeep Singh Dhingra, MBBS, MHS
Associate Research ScientistAbout
Research
Publications
Featured Publications
Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. European Heart Journal 2025, 46: 1044-1053. PMID: 39804243, PMCID: PMC12086686, DOI: 10.1093/eurheartj/ehae914.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFUK BiobankHF riskBrazilian Longitudinal Study of Adult HealthLongitudinal Study of Adult HealthBrazilian Longitudinal StudyRisk of new-onset HFPooled Cohort EquationsPrimary HF hospitalizationsHigher HF riskHarrell's C-statisticRisk of deathNew-onset HFCohort EquationsHealth systemComprehensive clinical evaluationAdult HealthHeart failureIncident HFHF hospitalizationBaseline HFC-statisticPrevent HFEnsemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Shankar S, Coppi A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD. Journal Of The American College Of Cardiology 2025, 85: 1302-1313. PMID: 40139886, PMCID: PMC12199746, DOI: 10.1016/j.jacc.2025.01.030.Peer-Reviewed Original ResearchConceptsStructural heart diseaseYale-New Haven HospitalTransthoracic echocardiogramRisk stratificationHeart failureLeft-sided valvular diseaseSevere left ventricular hypertrophyLeft ventricular ejection fractionReceiver-operating characteristic curveVentricular ejection fractionLeft ventricular hypertrophyHeart disease screeningELSA-BrasilEnsemble deep learning algorithmRisk of deathConvolutional neural network modelEjection fractionEnsemble deep learning approachVentricular hypertrophyDeep learning algorithmsNew Haven HospitalDeep learning approachValvular diseaseNeural network modelClinical cohortArtificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms
Dhingra L, Aminorroaya A, Pedroso A, Khunte A, Sangha V, McIntyre D, Chow C, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms. JAMA Cardiology 2025, 10: 574-584. PMID: 40238120, PMCID: PMC12004248, DOI: 10.1001/jamacardio.2025.0492.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFNew-onset HFHarrell's C-statisticProspective population-based cohortUK Biobank (UKBBrazilian Longitudinal StudyELSA-Brasil participantsC-statisticPopulation-based cohortIntegrated discrimination improvementReclassification improvementRisk of deathUKB participantsHealth systemRetrospective cohort studyDiscrimination improvementMain OutcomesLeft ventricular systolic dysfunctionHF riskUKBCohort studySingle-lead ECGIndependent of ageUse 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 dataMultinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM
Khera R, Dhingra L, Aminorroaya A, Li K, Zhou J, Arshad F, Blacketer C, Bowring M, Bu F, Cook M, Dorr D, Duarte-Salles T, DuVall S, Falconer T, French T, Hanchrow E, Horban S, Lau W, Li J, Liu Y, Lu Y, Man K, Matheny M, Mathioudakis N, McLemore M, Minty E, Morales D, Nagy P, Nishimura A, Ostropolets A, Pistillo A, Posada J, Pratt N, Reyes C, Ross J, Seager S, Shah N, Simon K, Wan E, Yang J, Yin C, You S, Schuemie M, Ryan P, Hripcsak G, Krumholz H, Suchard M. Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM. BMJ Medicine 2023, 2: e000651. PMID: 37829182, PMCID: PMC10565313, DOI: 10.1136/bmjmed-2023-000651.Peer-Reviewed Original ResearchType 2 diabetes mellitusSecond-line treatmentCardiovascular risk groupsDiabetes mellitusCardiovascular diseaseAntihyperglycaemic drugsLine treatmentRisk groupsObservational Health Data SciencesGlucagon-like peptide-1 receptor agonistsElectronic health recordsSodium-glucose cotransporter 2 inhibitorsCalendar year trendsPeptide-1 receptor agonistsUS databaseOutcomes of patientsCotransporter 2 inhibitorsAdministrative claims databaseSecond-line drugsHealth recordsSodium-glucose cotransporter-2 inhibitorsMedication useMetformin monotherapyGuideline recommendationsOutcome measuresCardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record
Dhingra L, Shen M, Mangla A, Khera R. Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record. The American Journal Of Cardiology 2023, 203: 136-148. PMID: 37499593, PMCID: PMC10865722, DOI: 10.1016/j.amjcard.2023.06.104.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsElectronic health recordsData elementsUnstructured data streamsUnstructured data elementsNatural language processingCommon data modelHealth recordsStructured data elementsComputer visionUnstructured dataData streamsHeterogeneity challengesSeamless deliveryData modelLanguage processingData storageFree textClinical narrativesComputational phenotypesOngoing workPatient informationRapid innovationSpecific expertiseConfidentialityOngoing innovation
2026
Wearable Devices and Data Sharing in the US
Pedroso A, Dhingra L, Aminorroaya A, Khera R. Wearable Devices and Data Sharing in the US. JAMA Network Open 2026, 9: e2617733. PMID: 42268609, PMCID: PMC13254737, DOI: 10.1001/jamanetworkopen.2026.17733.Peer-Reviewed Original ResearchConceptsHealth Information National Trends SurveyPopulation-based surveyUS adultsWearable device useSociodemographic subgroupsCardiovascular diseaseCommunity-dwelling US adultsRisk factorsWearable useNational Trends SurveyDevice useHealth care toolCVD risk factorsSurvey studyUS adult populationPotential of wearable devicesWearable dataPersonal health informationDaily useSharing of personal health informationAssociated with greater willingnessSubgroups of ageData sharingWearable devicesTrends SurveyThe Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation
Croon P, Dhingra L, Pedroso A, Khera R. The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation. Current Heart Failure Reports 2026, 23: 25. PMID: 42165933, DOI: 10.1007/s11897-026-00764-x.Peer-Reviewed Original ResearchConceptsImplementation of AIArtificial intelligence-based toolsLanguage modelElectronic health recordsWearable devicesWorkflow integrationReviewArtificial intelligenceClinician adoptionHealth recordsDetection of heart failureImplementationModel developmentInteroperabilityWearableDeploymentPractical frameworkIntelligenceDetectionWorkflowAIHeart failure careHeart failureOptimizationComparative Cardiovascular Effectiveness of Glucagon-Like Peptide 1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors in Diabetes Mellitus
Bu F, Wu R, Ostropolets A, Aminorroaya A, Chen H, Chai Y, Dhingra L, Falconer T, Hsu J, Kim C, Lau W, Man K, Minty E, Morales D, Nishimura A, Thangraraj P, Van Zandt M, Yin C, Khera R, Hripcsak G, Suchard M. Comparative Cardiovascular Effectiveness of Glucagon-Like Peptide 1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors in Diabetes Mellitus. Journal Of The American College Of Cardiology 2026, 87: 2963-2977. PMID: 41984016, PMCID: PMC13191718, DOI: 10.1016/j.jacc.2026.02.5123.Peer-Reviewed Original ResearchSodium-glucose cotransporter 2 inhibitorsGlucagon-like peptide 1 receptor agonistsPeptide 1 receptor agonistsComparative effectiveness assessmentCompare cardiovascular effectsCardiovascular diseaseGLP-1RACardiovascular effectsElectronic health record databaseSodium-glucoseRandom-effects meta-analysisGLP-1RAsHealth record databaseCardiovascular benefitsCardiovascular disease subgroupsCox proportional hazards modelsEstablished cardiovascular diseaseSecondary subgroup analysisSecond-line therapyEffects of glucagon-like peptide 1 receptor agonistsMeta-analytic estimatesProportional hazards modelType 2 diabetes mellitusCardiovascular outcome trialsPropensity score adjustmentOpen science requires trust and rigour: a framework for responsible evaluation of shared AI-ECG tools
Dhingra L, Croon P, Oikonomou E, Khera R. Open science requires trust and rigour: a framework for responsible evaluation of shared AI-ECG tools. European Heart Journal - Digital Health 2026, 7: ztag057. PMID: 41994368, PMCID: PMC13080934, DOI: 10.1093/ehjdh/ztag057.Commentaries, Editorials and Letters
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Yale School of Medicine Faculty To Present New Cardiovascular Research at National Cardiology Conference
- February 11, 2026
Using AI to Guide AI
- January 23, 2025
New AI Tool Identifies Risk of Future Heart Failure