The AIR-CPR Study: AI-Guided Chest Compressions (AIR-CPR)

February 19, 2026 updated by: Sheng-En Chu, Far Eastern Memorial Hospital

Utilizing Artificial Intelligence to Optimize Chest Compression Region During Cardio-pulmonary Resuscitation for Patients With Out-of-hospital Cardiac Arrest.

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

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 (Estimated)

255

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

  • Name: Sheng-En Chu, physician
  • Phone Number: 886-2-7728-1843
  • Email: ianchu300@msn.com

Study Locations

  • Taiwan
    • Banqiao
      • New Taipei City, Banqiao, Taiwan, 220
        • Recruiting
        • Far Eastern Memorinal Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

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.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
OHCA Patients Receiving AI-Enhanced Resuscitation.
Adult patients (20 years or older) with non-traumatic Out-of-Hospital Cardiac Arrest (OHCA) who receive Advanced Life Support (ALS) at the Far Eastern Memorial Hospital Emergency Department. This cohort provides the data for AI training (Years 1-2) and participates in the clinical validation of the AI-guided compression technique (Year 3).
Device: Device: AI-Enhanced Arterial Waveform Monitor (AIR-CPR App)
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.
Procedure: AI-Guided Chest Compression Repositioning
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.
Diagnostic test: Transesophageal Echocardiography (TEE)
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
AI Identification Accuracy of Aortic Valve Compression
Time Frame: Collected during the clinical testing phase and feasibility assessment (Years 2 and 3).
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.
Collected during the clinical testing phase and feasibility assessment (Years 2 and 3).

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Successful Avoidance of Aortic Valve Compression
Time Frame: During the clinical feasibility assessment (Year 3).
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.
During the clinical feasibility assessment (Year 3).
Time Consumed for Compression Adjustment
Time Frame: During the clinical feasibility assessment (Year 3).
The time interval between the first arterial waveform detection and the completion of the chest compression repositioning.
During the clinical feasibility assessment (Year 3).
Rate of Return of Spontaneous Circulation (ROSC)
Time Frame: From the start of the emergency department resuscitation until hospital discharge or death (up to approximately 30 days).
Incidence of ROSC and sustained ROSC (maintained for $\ge 20$ minutes), as well as survival rates to hospital admission and discharge.
From the start of the emergency department resuscitation until hospital discharge or death (up to approximately 30 days).
Favorable Neurologic Outcome at Discharge
Time Frame: At the time of hospital discharge (up to approximately 30 days).
Assessment of neurological status using the Cerebral Performance Category (CPC 1-2) or Modified Rankin Scale (mRS 0-2).
At the time of hospital discharge (up to approximately 30 days).
Chest Compression Fraction (CCF)
Time Frame: During the clinical feasibility assessment (Year 3).
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.
During the clinical feasibility assessment (Year 3).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Far Eastern Memorial Hospital

Collaborators

National Health Research Institutes, Taiwan

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 6, 2025

Primary Completion (Estimated)

December 31, 2027

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

February 19, 2026

First Submitted That Met QC Criteria

February 19, 2026

First Posted (Actual)

February 24, 2026

Study Record Updates

Last Update Posted (Actual)

February 24, 2026

Last Update Submitted That Met QC Criteria

February 19, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 113046-F

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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