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.

This three-year prospective study is designed to develop and clinically validate an "AI-Enhanced Arterial Waveform Monitor" to guide precision CPR.

1. Research Hypothesis and Objectives The study tests the hypothesis that AI can accurately predict aortic valve compression (confirmed by Transesophageal Echocardiography, TEE) by analyzing arterial pressure waveforms, thereby allowing rescuers to find the optimal compression site that avoids the aortic valve and maximizes cardiac output.

2. Implementation Phases

   The project is divided into five distinct stages:

   Case Preparation: Enrollment of 150 OHCA patients to collect synchronized TEE video and arterial pressure data.

   Arterial Waveform Detection Model: Development of an algorithm to automatically segment continuous pressure signals into single-compression waveform samples.

   Compression Region Detection Model: Training a YOLO v8-based model integrated with patient physiological data (age, sex, medical history) to distinguish between "compressed" and "non-compressed" aortic valve states.

   Clinical External Testing: Enrolling an additional 75 patients to verify model accuracy against TEE "gold standard" findings.

   Feasibility Assessment: Deploying the model as a "Resuscitation Support App" in 30 real-world clinical cases to evaluate its real-time guidance capability, speed, and impact on patient outcomes.

3. Technical Methodology

   Data Extraction: Using binarization and interpolation curve fitting to extract high-quality numerical data directly from physiological monitor screens.

   AI Architecture: Utilizing an improved YOLO v8 framework combined with an Attention-based architecture and Fully-connected neural networks to incorporate complex patient heterogeneities.

   Clinical Intervention: When the AI identifies aortic valve compression, rescuers will be prompted to adjust the compression location (typically downward and to the left) until the valve is no longer obstructed.

4. Outcome Measures The study will evaluate the Identification Success Rate (AI vs. TEE), Avoidance Success Rate (successful repositioning), and traditional resuscitation metrics including ROSC, survival to discharge, and favorable neurologic outcomes.

Inclusion Criteria:

1. Adults aged 20 years or older.
2. Patients with out-of-hospital cardiac arrest (OHCA) undergoing 3.cardiopulmonary resuscitation (CPR) in the emergency department.

Cardiac arrest caused by non-traumatic factors.

Exclusion Criteria:

1. Pregnant patients.
2. Patients with obvious signs of death.
3. Patients with a signed "Do Not Resuscitate" (DNR) order.
4. Patients requiring extracorporeal cardio-pulmonary resuscitation (ECPR).
5. Patients requiring Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA).
6. Cardiac arrest caused by massive hemorrhage, aortic emergencies, tension pneumothorax, cardiac tamponade, or pulmonary embolism.
7. History of severe aortic valve disease or previous aortic valve surgery.
8. Patients for whom TEE or femoral arterial catheterization is contraindicated.
9. Situations where the medical team is unable to perform TEE or femoral arterial catheterization during CPR.

The study population consists of adult patients experiencing non-traumatic out-of-hospital cardiac arrest who are transported to the Emergency Department of Far Eastern Memorial Hospital. This facility is the only medical center in New Taipei City and handles approximately 30% of the city's OHCA cases, ensuring a diverse and high-volume sample of patients receiving advanced life support.