Raghu et al., 2023
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|---|---|---|
| Sun et al. | Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives | |
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| Lee et al. | Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure | |
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| Ambavane et al. | Economic evaluation of the one-hour rule-out and rule-in algorithm for acute myocardial infarction using the high-sensitivity cardiac troponin T assay in the emergency department | |
| Ahmad et al. | Clinical implications of chronic heart failure phenotypes defined by cluster analysis | |
| Kelder et al. | Quantifying the added value of BNP in suspected heart failure in general practice: an individual patient data meta-analysis | |
| Jentzer et al. | Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients | |
| KR20220104144A (en) | ECG-Based Future Atrial Fibrillation Predictor Systems and Methods | |
| Ieki et al. | Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis | |
| Miller et al. | Stress CMR reduces revascularization, hospital readmission, and recurrent cardiac testing in intermediate-risk patients with acute chest pain | |
| Raghu et al. | ECG-guided non-invasive estimation of pulmonary congestion in patients with heart failure | |
| Li et al. | Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes | |
| Papadopoulou et al. | Clinical validation of an artificial intelligence-assisted algorithm for automated quantification of left ventricular ejection fraction in real time by a novel handheld ultrasound device | |
| Collins et al. | The combined utility of an S3 heart sound and B-type natriuretic peptide levels in emergency department patients with dyspnea | |
| Roalfe et al. | Development and initial validation of a simple clinical decision tool to predict the presence of heart failure in primary care: the MICE (Male, Infarction, Crepitations, Edema, MICE) rule | |
| US12102485B2 (en) | Surfacing insights into left and right ventricular dysfunction through deep learning | |
| Choi et al. | Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction | |
| Rafie et al. | Mortality prediction in cardiac intensive care unit patients: a systematic review of existing and artificial intelligence augmented approaches | |
| Ivanov et al. | Right atrial volume by cardiovascular magnetic resonance predicts mortality in patients with heart failure with reduced ejection fraction | |
| Seko et al. | Clinical impact of left and right axis deviations with narrow QRS complex on 3-year outcomes in a hospital-based population in Japan | |
| Dhingra et al. | Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms | |
| Tavares et al. | Clinical applicability and diagnostic performance of electrocardiographic criteria for left ventricular hypertrophy diagnosis in older adults | |
| Schlesinger et al. | Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor | |
| Moazeni et al. | Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure |