ENTERPRISE AI ANALYSIS
State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology
Artificial intelligence (AI) is an emerging technology that holds great promise for the field of clinical electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field.
Executive Impact Summary
The EHRA AI checklist was developed with expert input and validated retrospectively on published papers across AF management, SCD, and EP lab applications. Reporting quality varied, with key areas like trial registration and data handling being underreported (<20%). The checklist provides a structured framework to improve transparency and reproducibility, fostering more robust integration of AI into clinical EP.
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Average Reporting Compliance
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Key Reporting Gaps (Trial Reg./Data H.)
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Studies Reviewed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
The EHRA AI checklist was developed through a rigorous process involving expert panel development, literature review, and a two-round Delphi method to achieve consensus on items for reporting, reading, and understanding AI studies in clinical EP.
EP literature review and evidence synthesis
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Delphi process: Expert panel rates checklist
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Retrospective: Comparison with published AI research in clinical EP
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Increase scientific community reporting appropriateness in AI EP research
| Domain | Reporting Strengths | Key Reporting Gaps |
|---|---|---|
| AI in AF Management |
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| AI in Sudden Cardiac Death (SCD) |
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| AI in EP Lab Applications |
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Trial Registration Reporting in AF Management
One of the most significant reporting gaps identified was in trial registration. Only 2 of 31 papers (6%) in AF management addressed this critical item, highlighting a lack of transparency and potential barrier to reproducibility.
6% Papers addressing Trial Registration in AF ManagementAutomated AF Detection via Photoplethysmography (PPG)
PPG from wearables (smartphones, smartwatches) offers semi-continuous AF monitoring. AI algorithms are crucial for detecting AF in ambulatory settings. Key considerations for PPG-based AI include signal quality, noise reduction, and robust performance metrics (sensitivity, specificity, PPV, NPV, AUC-ROC). The intermittent nature of AF necessitates continuous monitoring to capture episodes.
Case: Wearable AI for AF Detection
Challenge: Intermittent AF and inaccurate burden estimates from short-term monitoring.
Solution: PPG-based AI algorithms in wearables for semi-continuous monitoring.
Impact: Potential for early detection and personalized management, though publication bias and robust validation remain key concerns.
ECG for AF Detection and Prediction
AI-enabled ECG analysis, particularly using Convolutional Neural Networks (CNNs) and Transformer Architectures, can detect AF signatures even during sinus rhythm. This allows for point-of-care assessment of AF risk and early, enhanced treatment strategies, especially for patients with embolic stroke of undetermined source. The increasing digitization of ECGs facilitates widespread computerized interpretation, moving beyond traditional ML models and predefined features.
Case: AI-Enhanced ECG Analysis
Challenge: Identifying subtle AF predispositions and improving diagnostic accuracy.
Solution: Deep learning (CNNs, Transformers) for ECG analysis, detecting AF signatures in sinus rhythm.
Impact: Early risk assessment, personalized treatment strategies, and improved outcomes for conditions like embolic stroke of undetermined source.
AI Models for SCD Prediction
AI offers personalized risk prediction for SCD, addressing limitations of current clinical criteria (e.g., LVEF <30-35% captures only 20% of at-risk SCDs). ECG-AI models show promise in low-risk populations (AUROC 0.82-0.90) and outperform traditional methods in moderate-risk heart failure patients. For high-risk ICD carriers, dynamic ECG changes and multimodal approaches (e.g., intracardiac EGMs, CMR, clinical data) are crucial. Remote monitoring and wearable devices provide continuous data for real-time prediction of imminent VA.
Case: Personalized SCD Risk Prediction
Challenge: Inadequate current risk stratification for SCD, missing a large portion of at-risk patients.
Solution: AI-enabled ECG models, multimodal DL models (ECG, CMR, clinical data), and remote monitoring for dynamic risk assessment.
Impact: Improved identification of high-risk patients, customization of preventive strategies, and better patient selection for ICD implantation.
Enterprise Process Flow
AI-guided software processes CMR and CT scans for pre-procedural planning, quantifying scar volume, predicting hemodynamic decompensation, and identifying VT isthmuses. It also enables automated classification of tissue channels (sub-endocardial, sub-epicardial, transmural) for guided ablation. This enhances precision, reduces procedure time, and improves outcomes in AF and VT ablation.
Data input (e.g., raw unipolar electrograms)
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Data preparation (e.g., splitting into training/testing sets)
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Deep learning model (e.g., 1D residual 18-layer CNN)
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Output (e.g., classification of electrograms into Non-FaST or FaST, potential arrhythmic substrate)
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Aid clinical decision-making on ablation target
AI in AF Ablation
AI (ML/DL) can automate pattern recognition of biological signals, reducing human feature engineering and inter-observer variability. Proprietary software like Volta classifies intracardiac EGMs in real-time to identify AF drivers beyond PVI. Digital twins, built from imaging data, simulate responses to ablation and predict recurrence, guiding optimal ablation targets. AI also aids in predicting AF recurrence post-ablation by analyzing pre-ablation imaging (CT, LGE-CMR) and clinical features.
Case: AI for Optimized AF Ablation
Challenge: Improving precision, reducing variability, and optimizing ablation targets for persistent AF.
Solution: Real-time AI classification of EGMs (Volta software), digital twins for personalized simulations, and AI analysis of imaging data for recurrence prediction.
Impact: Reduced need for further procedures, improved freedom from AF, and enhanced patient outcomes.
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Your AI Implementation Roadmap
A strategic timeline for integrating AI into your enterprise electrophysiology workflows.
Phase 1: Assessment & Strategy
Conduct a comprehensive needs assessment, identify key AI opportunities in EP, and develop a tailored implementation strategy. This includes data readiness evaluation and ethical considerations.
Phase 2: Pilot & Validation
Implement AI solutions in a pilot environment, focusing on specific EP applications like arrhythmia detection or ablation guidance. Rigorously validate performance using the EHRA AI checklist and internal/external datasets.
Phase 3: Integration & Scaling
Integrate validated AI tools into existing clinical workflows. Develop training programs for EP staff and scale the solutions across departments or multiple labs. Establish continuous monitoring and refinement processes.
Phase 4: Optimization & Futureproofing
Continuously monitor AI model performance, gather feedback, and iterate for optimization. Explore advanced AI applications, multimodal data integration, and future-proof your AI infrastructure against evolving clinical needs and technologies.
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