Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach - Summary - MDSpire

Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach

  • By

  • Mehak Gurnani

  • Konstantinos Patlatzoglou

  • Joseph Barker

  • Libor Pastika

  • Boroumand Zeidaabadi

  • Ibrahim Antoun

  • Riyaz Somani

  • G. Andre Ng

  • Paolo Inglese

  • Lara Curran

  • Declan O’Regan

  • Nicholas S. Peters

  • Daniel B. Kramer

  • Jonathan W. Waks

  • Arunashis Sau

  • Fu Siong Ng

  • December 4, 2025

  • 0 min

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Objective:

To identify novel atrial fibrillation (AF) phenotypes using an AI-enhanced electrocardiography method that captures electrophysiological differences, potentially improving patient outcomes.

Key Findings:
  • Five phenogroups stratified by future disease risk were identified, providing insights into patient management.
  • Phenogroup 2 reflected advanced AF with greater heart failure burden and mortality risk, highlighting the need for targeted interventions.
  • Paroxysmal phenogroups 4 and 5 differed in risk and ventricular structure, with phenogroup 5 exhibiting more adverse features, suggesting varying treatment approaches.
Interpretation:

The AI-ECG framework enhances traditional AF classifications by incorporating a risk-based dimension, supporting personalized patient care through tailored treatment strategies.

Limitations:
  • The study is based on a single-center dataset, which may limit generalizability; future studies should include multi-center data.
  • Potential biases in ECG data collection and patient selection may affect results; addressing these biases is crucial for validation.
Conclusion:

This study demonstrates the potential of AI-enhanced ECG methods to identify distinct AF phenotypes, which may improve risk stratification and management of AF patients, paving the way for personalized treatment plans.

Original Source(s)

Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach

  • by Mehak Gurnani, Konstantinos Patlatzoglou, Joseph Barker, Libor Pastika, Boroumand Zeidaabadi, Ibrahim Antoun, Riyaz Somani, G. Andre Ng, Paolo Inglese, Lara Curran, Declan O’Regan, Nicholas S. Peters, Daniel B. Kramer, Jonathan W. Waks, Arunashis Sau, Fu Siong Ng

  • December 4, 2025

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