Artificial Intelligence Techniques in Cardiac Neuromodulation: Mechanisms, Applications, and Pathways to Clinical...

Area of Science:

• Cardiovascular Electrophysiology
• Biomedical Engineering
• Artificial Intelligence in Medicine

Background:

• Cardiac neuromodulation aims to correct autonomic imbalance in cardiovascular diseases using methods like vagus nerve stimulation.
• Landmark trials show variable patient responses and highlight issues like dosing and patient phenotyping.
• Individualized approaches are needed for patient selection, therapy delivery, and monitoring.

Purpose of the Study:

• To review artificial intelligence (AI) techniques applicable to cardiac neuromodulation.
• To map AI methods to challenges in patient selection, real-time control, and response monitoring.
• To discuss the potential of AI in advancing personalized cardiac neuromodulation.

Main Methods:

• Narrative review of seven AI families: supervised learning, deep learning, representation learning, reinforcement learning, multimodal fusion, AI-informed digital twins, and explainable AI with federated learning.
• Summarized AI methods, their relevance to cardiac neuromodulation problems, and existing evidence.
• Mapped AI techniques to patient selection, real-time stimulation control, and longitudinal response monitoring.

Main Results:

• Representation learning shows promise for identifying vagus nerve stimulation responders.
• Reinforcement learning is suitable for closed-loop vagus nerve stimulation control.
• AI-informed digital twins can facilitate in silico testing of stimulation protocols.

Conclusions:

• AI offers powerful tools to overcome limitations in current cardiac neuromodulation therapies.
• Translating AI methods from adjacent fields into clinical trials is a key opportunity.
• Personalized AI-driven approaches can improve outcomes in cardiovascular disease management.