EP Trials Review
ECG Interpretations
ECG Interpretations: Accuracy, Alternative, and Advance
In a systematic review and meta-analysis of 78 studies that assessed the accuracy of physicians’ or medical students’ ECG interpretations in a test setting, the accuracy varied widely, ranging from 4% to 95%.
Ref: Accuracy of ECG Interpretations. JAMA Intern Med 2020.
ECG Interpretations Accuracy for Medical Students and Residents.
The median accuracy across all training levels was relatively low (54%), and scores increased as expected with progressive training and specialization; as follow:
ECG Interpretations Accuracy for Practicing Physicians and Cardiologists.
Automatic diagnosis of the 12-lead ECG using a deep neural network.
Ref: Automatic diagnosis of ECG using DNN. Nat Commun 2020.
Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. In this trial, the investigators trained DNN model in a dataset of more than 2 millions ECGs The DNN model was then tested employing 827 ECG tracings.
The DNN outperform cardiology resident in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%.
Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction.
Ref: AI-enabled ECG algorithm for LVSD. Circ EP 2020.
In this trial, an AI-enabled ECG algorithm applied retrospectively to a sample of 1,606 patients evaluated in an acute care setting for dyspnea. It effectively identifies LVSD and outperforms NT-proBNP.
Receiver operative characteristic (ROC) curve for identification of left ventricular ejection fraction (LVEF) ≤35%
The artificial intelligence-enabled ECG algorithm identified LVSD with an AUC of 0.89 (95% CI, 0.86–0.91) and accuracy of 85.9%. NT-proBNP alone at a cutoff value >800 identified new LVSD (EF≤35%) with an AUC of 0.80 (95% CI, 0.76–0.84).

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