Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques.

Area of Science:

• Cardiology
• Artificial Intelligence
• Machine Learning

Background:

• Traditional Cardiopulmonary Resuscitation (CPR) relies on manual techniques.
• There is a growing need for data-driven interventions to improve resuscitation success rates.
• Existing literature often lacks a specific focus on ML/AI applications within the CPR domain.

Purpose of the Study:

• To provide a comprehensive survey of Machine Learning (ML) and Artificial Intelligence (AI) applications in Cardiopulmonary Resuscitation (CPR).
• To synthesize interdisciplinary knowledge bridging resuscitation science and advanced ML techniques.
• To establish a foundation for future research and innovation in ML-enhanced CPR.

Main Methods:

• Classification of ML techniques into four key CPR tasks: rhythm analysis, outcome prediction, physiological modeling, and real-time event detection (e.g., Return of Spontaneous Circulation).
• Critical evaluation of emerging ML approaches, including Reinforcement Learning (RL) and transformer-based models.
• Analysis of implementation barriers, such as model interpretability, data scarcity, and clinical deployment challenges, with a focus on eXplainable AI (XAI).

Main Results:

• A structured taxonomy categorizing ML applications across critical CPR functions.
• Identification of advanced ML models (RL, transformers) showing promise for CPR.
• Discussion of challenges and the importance of XAI for clinical adoption in high-stakes CPR scenarios.

Conclusions:

• ML and AI represent a paradigm shift, moving CPR towards intelligent, data-driven interventions.
• Addressing implementation barriers and leveraging XAI are crucial for the successful integration of ML in CPR.
• This survey provides a forward-looking agenda for improving CPR reliability and effectiveness through advanced computational techniques.