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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Imaging Studies I: CT and MRI

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Artificial Intelligence in Veterinary Imaging: An Overview.

Ana Inês Pereira1, Pedro Franco-Gonçalo1,2,3, Pedro Leite4

  • 1Department of Veterinary Science, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal.

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|May 26, 2023
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Summary
This summary is machine-generated.

This study provides veterinarians with a guide to artificial intelligence (AI) and machine learning (ML) in veterinary medical imaging. It explains AI/ML concepts for automated image analysis and diagnosis support in animals.

Keywords:
artificial intelligencedeep learningmachine learningveterinary imaging

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Area of Science:

  • Veterinary Medical Imaging
  • Artificial Intelligence in Medicine
  • Machine Learning Applications

Background:

  • Medical image evaluation is subjective and complex, necessitating automated analysis.
  • Artificial intelligence (AI) and machine learning (ML) offer solutions for objective image interpretation.
  • Existing research applies AI/ML to assist veterinary diagnostics.

Purpose of the Study:

  • To provide veterinary professionals with a foundational understanding of AI and ML.
  • To detail methodologies for developing AI/ML software for veterinary medical imaging.
  • To review existing literature on AI/ML applications in animal imaging diagnosis.

Main Methods:

  • Explanation of core AI/ML concepts: deep learning, convolutional neural networks, transfer learning.
  • Review of published research on AI/ML in veterinary imaging diagnosis.
  • Focus on practical application and benefit for veterinarians.

Main Results:

  • The study outlines methodologies for creating AI/ML-powered diagnostic tools.
  • It covers applications across various animal body systems: musculoskeletal, thoracic, nervous, and abdominal.
  • The guide is tailored for veterinary technicians and professionals.

Conclusions:

  • AI and ML methodologies can significantly enhance the accuracy and efficiency of veterinary medical imaging analysis.
  • This guide empowers veterinarians to leverage AI/ML for improved diagnostic capabilities.
  • Understanding these technologies is crucial for the future of veterinary diagnostics.