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Aortic valve regurgitation (AR) occurs when the aortic valve fails to close properly, allowing blood to flow backward from the aorta into the left ventricle. This backflow can result in two distinct clinical presentations: acute and chronic AR, each characterized by its own set of symptoms and physical findings.Acute Aortic RegurgitationAcute AR presents with a sudden onset of severe symptoms. Patients typically experience profound dyspnea (shortness of breath), chest pain, and signs of left...
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Related Experiment Video

Updated: Oct 17, 2025

Novel and Innovative Hybrid Technique for Type A Aortic Dissection
06:26

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Published on: March 28, 2025

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A Deep-Learning Algorithm-Enhanced System Integrating Electrocardiograms and Chest X-rays for Diagnosing Aortic

Wei-Ting Liu1, Chin-Sheng Lin2, Tien-Ping Tsao3

  • 1Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.

The Canadian Journal of Cardiology
|October 7, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning models using ECG and CXR effectively detect aortic dissection (AD), outperforming physicians. These AI tools aid in diagnosing chest pain, improving patient outcomes.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Cardiology

Background:

  • Chest pain is a common symptom of aortic dissection (AD), often misdiagnosed as other cardiopulmonary conditions.
  • Accurate and timely diagnosis of AD is critical due to its high mortality rate.

Purpose of the Study:

  • To develop and evaluate deep learning models (DLMs) utilizing electrocardiography (ECG) and chest x-ray (CXR) features for detecting AD.
  • To compare the diagnostic performance of DLMs against human physicians in identifying AD.

Main Methods:

  • Retrospective development and validation of DLMs using a large cohort of emergency department (ED) patient records (over 43,000).
  • Training DLMs on ECG and CXR data, with subsequent independent validation.
  • Conducting human-machine competitions and prospective evaluation of the DLM-enabled diagnostic process.

Main Results:

  • DLMs achieved high diagnostic performance, with AUCs of 0.918 for ECG and 0.857 for CXR in human-machine competition, surpassing physicians.
  • The integrated model demonstrated strong performance in validation cohorts (AUCs up to 0.960 for AD Type A) and achieved 100% sensitivity for AD Type A.
  • DLMs showed improved predictive accuracy in patients with chest pain and specific laboratory results during prospective evaluation.

Conclusions:

  • Deep learning models show significant potential as decision-support tools for identifying aortic dissection.
  • These AI-driven tools can assist clinicians in the differential diagnosis of acute chest pain, potentially improving diagnostic accuracy and speed.