The AIR-CPR project aims to improve survival rates for patients with Out-of-Hospital Cardiac Arrest (OHCA) by utilizing Artificial Intelligence (AI) to optimize chest compression locations. Current guidelines recommend a standardized compression point (the lower half of the sternum), yet recent research indicates that this position can compress the aortic valve in approximately 48.7% of patients, significantly reducing the chances of successful resuscitation.
This study will develop a deep learning model based on YOLO v8 to analyze real-time arterial pressure waveforms to identify proper aortic valve opening and closing. By identifying specific waveform features that humans cannot easily distinguish, the AI will guide rescuers to adjust the compression site-typically toward the left ventricle-to ensure optimal blood output. The project seeks to transform CPR from a standardized "one-size-fits-all" approach into a personalized, precision medicine intervention.
Adults aged 20 years or older.
Patients with out-of-hospital cardiac arrest (OHCA) undergoing 3.cardiopulmonary resuscitation (CPR) in the emergency department.
Pregnant patients.
Patients with obvious signs of death.
Patients with a signed "Do Not Resuscitate" (DNR) order.
Patients requiring extracorporeal cardio-pulmonary resuscitation (ECPR).
Far Eastern Memorial Hospital
Lead sponsor
National Health Research Institutes, Taiwan
Collaborator