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 algorithmPDFDetection of ATTR cardiac amyloidosis using a novel artificial intelligence algorithm for wearable-adapted noisy single-lead electrocardiograms
Sangha V, Oikonomou E, Khunte A, Miller E, Khera R. Detection of ATTR cardiac amyloidosis using a novel artificial intelligence algorithm for wearable-adapted noisy single-lead electrocardiograms. European Heart Journal 2024, 45: ehae666.3438. DOI: 10.1093/eurheartj/ehae666.3438.Peer-Reviewed Original ResearchReal-world noiseSingle-lead ECGArtificial intelligence algorithmsMultiple signal-to-noise ratiosCommunity-dwelling adultsSignal-to-noise ratioIntelligence algorithmsATTR-CMMatched controlsECG signalsDevelopment cohortPreventive careHealthcare servicesBlack adultsHospital systemCommunity screeningAlgorithmBone scintigraphy scansATTR cardiac amyloidosisPrevalence levelsOnset of symptomsPositive predictive valuePrevalenceAI-ECG algorithmSex-matched