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 algorithmPDFArtificial intelligence-guided screening of under-recognized cardiomyopathies adapted for point-of-care echocardiography
Oikonomou E, Holste G, Coppi A, Mcnamara R, Nadkarni G, Krumholz H, Wang Z, Miller E, Khera R. Artificial intelligence-guided screening of under-recognized cardiomyopathies adapted for point-of-care echocardiography. European Heart Journal 2024, 45: ehae666.157. DOI: 10.1093/eurheartj/ehae666.157.Peer-Reviewed Original ResearchConvolutional neural networkMulti-labelState-of-the-art performanceState-of-the-artCustom loss functionDeep learning modelsAI frameworkNeural networkLoss functionAutomated metricsLearning modelsAugmentation approachVideoAcquisition qualityAdvanced protocolsPoint-of-care ultrasonographyImagesTransthoracic echocardiogramClassifierATTR-CMAlgorithmNetworkAI screeningAcquisitionPresence of severe AS