https://doi.org/10.55788/84de9f2e
It would be helpful for clinicians and patients if the mechanism of arrhythmias could be identified via ECGs with a high degree of certainty, according to Dr Arunashis Sau (Imperial College London, UK) [1]. Two main categories of atrial arrhythmias are CTI-dependent atrial flutter and non-CTI dependent atrial tachycardia. The current study aimed to train a convolutional neural network to discriminate between these 2 categories of arrhythmias. The model was compared with expert assessments, using an invasive electrophysiology study as the source of truth. Collected were 13,557 ECGs from 288 patients. The training data set consisted of 13,500 ECGs from 231 patients and the test set included 57 ECGs from 57 patients.
The model achieved an accuracy of 86%, which was a significantly higher accuracy than the electrophysiologist assessment (79%) and a numerically higher accuracy than the electrophysiologist consensus (81%). The area under the curve of the model was 0.94. Notably, experts were more likely to incorrectly diagnose an atrial flutter case as being atrial tachycardia than the AI model (34.5% vs 10.3%). According to Dr Sau, this finding could have significant implications, given that CTI-dependent atrial flutter is more amendable to catheter ablation. Furthermore, when the model and electrophysiologist consensus agreed, the prediction accuracy was 100%. “This result indicates that the use of this model with human-in-the-loop provides powerful results,” argued Dr Sau.
“We successfully trained a neural network to distinguish CTI-dependent atrial flutter from atrial tachycardia, with a performance that is at least equivalent to human expert performance,” concluded Dr Sau. “Other studies will be conducted to further analyse the use of AI in ECG-based diagnosing of patients with arrhythmias.”
- Sau A, et al. Classification of organised atrial arrythmias using explainable artificial intelligence. E-cardiology award session, EHRA 2022, 3–5 April, Copenhagen, Denmark.
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Table of Contents: EHRA 2022
Featured articles
Letter from the Editor
Diagnostics and Prevention
Cardiac magnetic resonance imaging improves prediction of post-MI sudden cardiac death
AI model accurately predicts sudden cardiac death in overall population
AI model accurately discriminates between arrhythmias
Impact of AF screening on stroke prevention influenced by systolic blood pressure
Developments in Devices
Conduction system pacing potential alternative for biventricular pacing in heart failure
Left bundle branch area pacing is a feasible technique for HF and bradyarrhythmia
Focus on the efficacy of cardiac resynchronisation therapy in HF plus concomitant AF
RESET: No survival benefit of CRT-defibrillator over CRT-pacemaker in heart failure
Insertable cardiac monitors effective for AF detection in cryptogenic stroke
Updates on Ablation
First results of the POWER FAST III trial
Real-world safety results on pulsed-field ablation with pentaspline catheter
VANISH: Ablation reduces shock burden compared with anti-arrhythmic drug in ventricular tachycardia
Low AF recurrence rates after PVI using pulsed-field ablation
Pulsed-field ablation reduces neurocardiac damage versus cryoballoon ablation
Ultrasound-guided femoral venipuncture reduces complications in catheter ablation
News on Atrial Fibrillation
Sex differences revealed in AF determinants and AF progression
Early rhythm-control therapy efficacious in men and women with AF
Progression in remote app-based monitoring of atrial fibrillation
Other Topics
Benefits of SGLT2 inhibitors may extend beyond HF-associated outcomes
Updates on anti-arrhythmic agents
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June 1, 2022
Letter from the Editor
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