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Related Experiment Video

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Cardiac phase detection in echocardiography using convolutional neural networks.

Moomal Farhad1, Mohammad Mehedy Masud2, Azam Beg1

  • 1College of Information Technology, United Arab Emirates University, Al Ain, P.O. Box 15551, United Arab Emirates.

Scientific Reports
|June 1, 2023
PubMed
Summary
This summary is machine-generated.

DeepPhase, a deep learning model, automates cardiac phase detection in echocardiography, improving accuracy and efficiency for cardiologists. This AI tool identifies end-systolic (ES) and end-diastolic (ED) phases, reducing manual errors in heart condition assessments.

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Echocardiography is crucial for assessing heart conditions, relying on accurate identification of end-systolic (ES) and end-diastolic (ED) cardiac phases.
  • Manual detection of these phases is time-consuming and prone to diagnostic errors, necessitating automated solutions.
  • Non-essential frames (Non-ESED) can complicate manual phase identification.

Purpose of the Study:

  • To develop and evaluate DeepPhase, a deep learning model for automated detection of ES, ED, and Non-ESED cardiac phases in echocardiography.
  • To improve the efficiency and accuracy of cardiac phase identification, thereby minimizing diagnostic errors.
  • To provide a tool that assists cardiology personnel in analyzing echocardiographic images.

Main Methods:

  • A convolutional neural network (CNN) model, DeepPhase, was developed using echocardiography images.
  • The model was trained to identify ES, ED, and Non-ESED phases without requiring left ventricle segmentation or electrocardiograms.
  • A novel cropping technique was introduced to enhance model performance and real-world applicability.

Main Results:

  • DeepPhase demonstrated superior performance compared to existing techniques.
  • The model achieved an area under the curve (AUC) of 0.96 on the CAMUS dataset.
  • An AUC of 0.82 was achieved on the newly collected CardiacPhase dataset.

Conclusions:

  • DeepPhase offers an automated and efficient method for cardiac phase detection in echocardiography.
  • The model shows significant potential for reducing diagnostic errors and improving workflow in cardiology.
  • The proposed deep learning approach and cropping technique are effective for real-world clinical applications.