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Imaging Studies for Cardiovascular System III: X-Ray01:20

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Pericardial Effusion Detection on Post-Mortem Computed Tomography Images Using Convolutional Neural Networks.

Haoyu Kong1, Jia Rong2, Chris Bain1

  • 1Department of Human-Centred Computing, Faculty of Information Technology, Monash University, Australia.

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This study introduces an automated system for detecting pericardial effusion in post-mortem CT scans, improving accuracy and speed in forensic investigations.

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Pericardial effusion detectionconvolutional neural networkpost-mortem computed tomography

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Area of Science:

  • Forensic Radiology
  • Medical Imaging Analysis
  • Artificial Intelligence in Medicine

Background:

  • Pericardial effusion is a critical indicator of underlying disease and potential fatality.
  • Post-mortem computed tomography (PMCT) aids forensic death investigations.
  • A shortage of trained radiologists hinders efficient PMCT analysis for pericardial effusion.

Purpose of the Study:

  • To develop an automated framework (PEAD) for detecting pericardial effusion in whole-body PMCT images.
  • To enhance the efficiency and accuracy of forensic image analysis.
  • To address the limitations of manual interpretation by radiologists.

Main Methods:

  • Proposed a Pericardial Effusion Automatic Detection (PEAD) framework.
  • Modified established convolutional neural network architectures (VGG and ResNet) for PMCT image characteristics.
  • Implemented image filtering to exclude irrelevant scans lacking cardiac anatomy.

Main Results:

  • The PEAD framework demonstrated effectiveness in processing PMCT images.
  • Modified VGG and ResNet models achieved higher detection accuracy compared to standard architectures.
  • The modified models offered reduced processing times, enhancing efficiency.

Conclusions:

  • The proposed PEAD framework and modified neural networks significantly improve pericardial effusion detection in PMCT.
  • This automated approach offers a valuable tool for forensic radiology, overcoming radiologist scarcity.
  • The system enhances diagnostic accuracy and processing speed in post-mortem imaging analysis.