2025
Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning
Sangha V, Dhingra L, Aminorroaya A, Croon P, Sikand N, Sen S, Martinez M, Maron M, Krumholz H, Asselbergs F, Oikonomou E, Khera R. Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning. Nature Cardiovascular Research 2025, 4: 991-1000. PMID: 40696040, DOI: 10.1038/s44161-025-00685-3.Peer-Reviewed Original ResearchCardiac magnetic resonance markers of pre-clinical hypertrophic and dilated cardiomyopathy in genetic variant carriers
Croon P, van Vugt M, Allaart C, Ruijsink B, Elliott P, Asselbergs F, Khera R, Bezzina C, Winter M, Schmidt A. Cardiac magnetic resonance markers of pre-clinical hypertrophic and dilated cardiomyopathy in genetic variant carriers. BMC Medicine 2025, 23: 421. PMID: 40660250, PMCID: PMC12261545, DOI: 10.1186/s12916-025-04226-4.Peer-Reviewed Original ResearchConceptsCardiac magnetic resonance imagingCardiac magnetic resonance imaging measuresCardiac magnetic resonance markersDilated CardiomyopathyHypertrophic cardiomyopathyHeart failureUK Biobank participantsAtrial fibrillationLeft ventricular (LV) ejection fractionMitral annular plane systolic excursionAnnular plane systolic excursionLV) ejection fractionFunctional cardiac abnormalitiesEnd-systolic volumeCardiac risk factorsCardiomyopathy-associated variantsAssociated with incident AFAssociated with TTNIncident atrial fibrillationLV-ESVIMagnetic resonance imagingRV-ESVRV EFGenetic variant carriersSystolic excursionArtificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study
Oikonomou E, Vaid A, Holste G, Coppi A, McNamara R, Baloescu C, Krumholz H, Wang Z, Apakama D, Nadkarni G, Khera R. Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study. The Lancet Digital Health 2025, 7: e113-e123. PMID: 39890242, PMCID: PMC12084816, DOI: 10.1016/s2589-7500(24)00249-8.Peer-Reviewed Original ResearchConceptsYale New Haven Health SystemPoint-of-care ultrasonographyMount Sinai Health SystemTransthyretin amyloid cardiomyopathyArtificial intelligenceHealth systemAmyloid cardiomyopathyHypertrophic cardiomyopathyRetrospective cohort of individualsCardiomyopathy casesTesting artificial intelligenceConvolutional neural networkSinai Health SystemCohort of individualsOpportunistic screeningHypertrophic cardiomyopathy casesMulti-labelPositive screenAI frameworkEmergency departmentMortality riskNeural networkLoss functionCardiac ultrasonographyAugmentation approach
2024
A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers
Dhingra L, Sangha V, Aminorroaya A, Bryde R, Gaballa A, Ali A, Mehra N, Krumholz H, Sen S, Kramer C, Martinez M, Desai M, Oikonomou E, Khera R. A Multicenter Evaluation of the Impact of Therapies on Deep Learning-Based Electrocardiographic Hypertrophic Cardiomyopathy Markers. The American Journal Of Cardiology 2024, 237: 35-40. PMID: 39581517, PMCID: PMC11761372, DOI: 10.1016/j.amjcard.2024.11.028.Peer-Reviewed Original ResearchCleveland Clinic FoundationHypertrophic cardiomyopathyMedian follow-up periodHypertrophic cardiomyopathy therapyMonitoring Treatment ResponseFollow-up periodImpact of therapyAtlantic Health SystemLack of improvementOral alternativePost-SRTMedical therapyTreatment responseMulticenter evaluationInterventricular septumPercutaneous reductionMavacamtenTherapyPatientsClinic FoundationPoint-of-care monitoringECGECG imagesScoresHealth system
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