Atrial Fibrillation Treatment Stratification Based on Artificial Intelligence-Driven Analysis of the...

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Current atrial fibrillation (AF) treatment strategies have limited efficacy and predictive capabilities.
• Artificial Intelligence (AI) offers a promising approach for predicting treatment outcomes.

Purpose of the Study:

• To develop an AI-driven platform for patient stratification using Electrocardiographic Imaging (ECGI) biomarkers and clinical data.
• To evaluate and predict optimal treatment strategies for atrial fibrillation patients.

Main Methods:

• 204 AF patients were analyzed using ECGI recordings to derive frequency and rotational biomarkers.
• A clustering algorithm integrated AF type, ECGI complexity score, and treatment type (rhythm or rate control) for outcome prediction.
• The algorithm's predictive performance was assessed using Area Under the Curve (AUC) and a 20% test set.

Main Results:

• AI-driven stratification identified five patient clusters with varying electrophysiological complexity and treatment outcomes.
• Low complexity patterns predicted better outcomes post-ablation, irrespective of AF duration.
• Higher complexity scores correlated with increased AF recurrence risk, impacting both paroxysmal and persistent AF.
• The AI algorithm achieved an AUC of 0.73, significantly outperforming conventional classification (AUC: 0.58).
• The algorithm demonstrated 90% prediction success on a validation test set.

Conclusions:

• AI analysis combining clinical data and ECGI biomarkers enhances AF treatment stratification.
• This approach improves predictive performance compared to conventional classification methods.
• The developed AI platform offers a more precise tool for optimizing AF patient management.