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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans
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Diagnosing Drowning in Postmortem CT Images Using Artificial Intelligence.

Terumasa Ogawara1, Akihito Usui2, Noriyasu Homma3

  • 1Department of Forensic Medicine, Tohoku University Graduate School of Medicine.

The Tohoku Journal of Experimental Medicine
|November 17, 2022
PubMed
Summary

Artificial intelligence (AI) can help diagnose drowning from postmortem computed tomography (CT) scans, achieving a high area under the curve (AUC) of 0.95. However, AI accuracy decreases in cases with resuscitation or emphysema.

Keywords:
artificial intelligenceautopsydeep learningdrowningpostmortem computed tomography

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Area of Science:

  • Forensic Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Postmortem computed tomography (CT) imaging reveals lung features in drowning cases.
  • Distinguishing subtle CT image differences can be challenging for inexperienced forensic pathologists.

Purpose of the Study:

  • To evaluate the performance of artificial intelligence (AI) with deep learning for diagnosing drowning in postmortem CT images.
  • To assess AI as a complementary tool for forensic pathology in drowning investigations.

Main Methods:

  • A modified AlexNet deep learning architecture was employed.
  • High-resolution CT chest images from 153 drowned and 160 non-drowned individuals were analyzed.
  • The AI model outputted a drowning probability for each image component.

Main Results:

  • The AI model achieved an area under the receiver operating characteristic curve (AUC) of 0.95.
  • Accuracy was 81% for cases with resuscitation and 92% for cases without.
  • The AI demonstrated strong performance in differentiating drowning from non-drowning cases.

Conclusions:

  • The proposed AI architecture is a powerful complementary tool for diagnosing drowning in postmortem CT scans.
  • Caution is advised when using AI for drowning diagnosis in cases involving cardiopulmonary resuscitation or emphysema.
  • AI shows significant potential to aid forensic pathologists in drowning case analysis.