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.
Study Type
OBSERVATIONAL
Enrollment
255
A deep learning application based on the YOLO v8 architecture that analyzes real-time arterial pressure waveforms from a femoral A-line. It identifies whether the current chest compression location is causing aortic valve compression (as confirmed by TEE) and provides immediate feedback to the resuscitation team.
When the AI application indicates aortic valve compression, the rescuer adjusts the mechanical chest compression (LUCAS) position. Based on literature and AI feedback, the adjustment typically involves moving the compression point downward and toward the left parasternal line to avoid the aortic valve and optimize left ventricular output.
Used as the "Gold Standard" throughout the study. TEE is performed during CPR to record the actual opening and closing of the aortic valve and the deformation of cardiac chambers, providing the labels for AI training and the verification for clinical testing.
Far Eastern Memorinal Hospital
New Taipei City, Banqiao, Taiwan
RECRUITINGAI Identification Accuracy of Aortic Valve Compression
The accuracy of the AI model in identifying whether the aortic valve is compressed or open during CPR, using Transesophageal Echocardiography (TEE) as the gold standard for verification.
Time frame: Collected during the clinical testing phase and feasibility assessment (Years 2 and 3).
Successful Avoidance of Aortic Valve Compression
The percentage of cases where the resuscitation team successfully adjusted the chest compression location to stop aortic valve compression based on AI app feedback, confirmed by TEE.
Time frame: During the clinical feasibility assessment (Year 3).
Time Consumed for Compression Adjustment
The time interval between the first arterial waveform detection and the completion of the chest compression repositioning.
Time frame: During the clinical feasibility assessment (Year 3).
Rate of Return of Spontaneous Circulation (ROSC)
Incidence of ROSC and sustained ROSC (maintained for $\\ge 20$ minutes), as well as survival rates to hospital admission and discharge.
Time frame: From the start of the emergency department resuscitation until hospital discharge or death (up to approximately 30 days).
Favorable Neurologic Outcome at Discharge
Assessment of neurological status using the Cerebral Performance Category (CPC 1-2) or Modified Rankin Scale (mRS 0-2).
Time frame: At the time of hospital discharge (up to approximately 30 days).
Chest Compression Fraction (CCF)
The proportion of total resuscitation time during which chest compressions were performed, ensuring that AI-guided adjustments do not negatively impact the continuity of compressions.
Time frame: During the clinical feasibility assessment (Year 3).
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