2024
A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device
Sangha V, Dhingra L, Khera R. A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device. European Heart Journal 2024, 45: ehae666.3437. DOI: 10.1093/eurheartj/ehae666.3437.Peer-Reviewed Original ResearchDeep learning modelsPDF outputArtificial intelligenceLearning modelsConvolutional neural networkPortable devicesPortable ECG deviceMultilabel modelEfficientNet-B3Neural networkECG outputWearable ECGHeld-out subsetCurrent algorithmsSingle-lead ECGSynthetic ECGECG deviceWearableClinical ECGClinical labelsAlgorithmCommercial devicesDetection of conductivityNo current algorithmPDF
2022
Automated multilabel diagnosis on electrocardiographic images and signals
Sangha V, Mortazavi BJ, Haimovich AD, Ribeiro AH, Brandt CA, Jacoby DL, Schulz WL, Krumholz HM, Ribeiro ALP, Khera R. Automated multilabel diagnosis on electrocardiographic images and signals. Nature Communications 2022, 13: 1583. PMID: 35332137, PMCID: PMC8948243, DOI: 10.1038/s41467-022-29153-3.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArtificial intelligenceApplication of AISignal-based dataSignal-based modelElectrocardiographic imagesECG imagesGrad-CAMImage-based modelsNeural networkDiagnosis modelECG signalsImagesClinical labelsValidation setLabelsExternal validation setMultilabelIntelligenceNetworkApplicationsModelBroad useSetBroader setting