Characterizing the progression of subclinical cardiac amyloidosis through artificial intelligence applied to electrocardiographic images and echocardiograms
Oikonomou E, Sangha V, Coppi A, Krumholz H, Miller E, Khera R. Characterizing the progression of subclinical cardiac amyloidosis through artificial intelligence applied to electrocardiographic images and echocardiograms. European Heart Journal 2024, 45: ehae666.2089. DOI: 10.1093/eurheartj/ehae666.2089.Peer-Reviewed Original ResearchDiagnosis of ATTR-CMATTR-CMBone scintigraphy scansClinical diagnosisTransthyretin amyloid cardiomyopathyMonths of diagnosisSex-matched controlsElectrocardiographic (ECGIndolent courseCardiac amyloidosisScintigraphy scanAmyloid cardiomyopathyEchocardiographic studiesAI-ECGEchocardiogramEventual diagnosisDetect longitudinal changesConfirmatory testDiagnosisClinical diseasePercentage of individualsLongitudinal changesECGMedianMonthsArtificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis
Sangha V, Oikonomou E, Krumholz H, Miller E, Khera R. Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis. European Heart Journal 2024, 45: ehae666.3436. DOI: 10.1093/eurheartj/ehae666.3436.Peer-Reviewed Original ResearchATTR-CMBone scintigraphy scansTransthyretin amyloid cardiomyopathyPositive predictive valueAI-ECG algorithmCardiac amyloidosisScintigraphy scanAmyloid cardiomyopathyAI-ECGSex-matchedDevelopment cohortMyocardial remodelingUnder-diagnosedUnder-treatedMatched controlsPredictive valueUnder-recognizedTransthyretin stabilizersConvolutional neural networkPatientsECGArtificial intelligenceHospitalPrevalenceTransthyretin