A deep neural network (VA-ResNet-50) classified ambulatory ECGs for ventricular arrhythmia capability with an accuracy of 0.76 and a relative risk of 2.87 (95% CI 1.41-5.81).
Case-Control (n=270)
Yes
Can an artificial intelligence deep neural network (VA-ResNet-50) accurately predict the risk of ventricular arrhythmia based solely on a standard ambulatory electrocardiogram?
An open-source deep neural network can accurately predict the risk of lethal ventricular arrhythmias from standard 24-hour ambulatory ECGs, demonstrating potential to improve upon current clinical guidelines for ICD allocation.
Effect estimate: RR 2.87 (95% CI 1.41-5.81)
Aims: European and American clinical guidelines for implantable cardioverter defibrillators are insufficiently accurate for ventricular arrhythmia (VA) risk stratification, leading to significant morbidity and mortality. Artificial intelligence offers a novel risk stratification lens through which VA capability can be determined from the electrocardiogram (ECG) in normal cardiac rhythm. The aim of this study was to develop and test a deep neural network for VA risk stratification using routinely collected ambulatory ECGs. Methods and results: A multicentre case-control study was undertaken to assess VA-ResNet-50, our open source ResNet-50-based deep neural network. VA-ResNet-50 was designed to read pyramid samples of three-lead 24 h ambulatory ECGs to decide whether a heart is capable of VA based on the ECG alone. Consecutive adults with VA from East Midlands, UK, who had ambulatory ECGs as part of their NHS care between 2014 and 2022 were recruited and compared with all comer ambulatory electrograms without VA. Of 270 patients, 159 heterogeneous patients had a composite VA outcome. The mean time difference between the ECG and VA was 1.6 years (⅓ ambulatory ECG before VA). The deep neural network was able to classify ECGs for VA capability with an accuracy of 0.76 (95% confidence interval 0.66-0.87), F1 score of 0.79 (0.67-0.90), area under the receiver operator curve of 0.8 (0.67-0.91), and relative risk of 2.87 (1.41-5.81). Conclusion: from the ambulatory ECGs is hypothesized to capture autonomic activity. We encourage groups to build on this open-source model. Question: Can artificial intelligence (AI) be used to predict whether a person is at risk of a lethal heart rhythm, based solely on an electrocardiogram (an electrical heart tracing)? Findings: In a study of 270 adults (of which 159 had lethal arrhythmias), the AI was correct in 4 out of every 5 cases. If the AI said a person was at risk, the risk of lethal event was three times higher than normal adults. Meaning: In this study, the AI performed better than current medical guidelines. The AI was able to accurately determine the risk of lethal arrhythmia from standard heart tracings for 80% of cases over a year away-a conceptual shift in what an AI model can see and predict. This method shows promise in better allocating implantable shock box pacemakers (implantable cardioverter defibrillators) that save lives.
Barker et al. (Sat,) conducted a case-control in Ventricular arrhythmia (n=270). VA-ResNet-50 deep neural network vs. All comer ambulatory electrograms without ventricular arrhythmia was evaluated on Ventricular arrhythmia capability classification (RR 2.87, 95% CI 1.41-5.81). A deep neural network (VA-ResNet-50) classified ambulatory ECGs for ventricular arrhythmia capability with an accuracy of 0.76 and a relative risk of 2.87 (95% CI 1.41-5.81).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: