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Veterinary sciences2022; 9(11); 620; doi: 10.3390/vetsci9110620

A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging.

Abstract: Great advances have been made in human health care in the application of radiomics and artificial intelligence (AI) in a variety of areas, ranging from hospital management and virtual assistants to remote patient monitoring and medical diagnostics and imaging. To improve accuracy and reproducibility, there has been a recent move to integrate radiomics and AI as tools to assist clinical decision making and to incorporate it into routine clinical workflows and diagnosis. Although lagging behind human medicine, the use of radiomics and AI in veterinary diagnostic imaging is becoming more frequent with an increasing number of reported applications. The goal of this paper is to provide an overview of current radiomic and AI applications in veterinary diagnostic imaging.
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Publication Date: 2022-11-08 PubMed ID: 36356097PubMed Central: PMC9693121DOI: 10.3390/vetsci9110620Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article explores the advancements in radiomics and artificial intelligence and their growing application in veterinary diagnostic imaging. It compares the slower adoption rate within veterinary care to that in human healthcare and discusses the potential benefits of these technologies in the former.

Understanding Radiomics and Artificial Intelligence

Radiomics is the extraction of a large number of quantitative features from medical images such as CTs, MRIs, and Ultrasounds. These features, which can potentially reveal disease characteristics that are undetectable by the naked eye, can be used for prognosis or predicting treatment response.

AI, on the other hand, includes machine learning techniques that can integrate these high-dimensional data with clinical and genetic data for decision support. In the setting of medical imaging, AI can be used to automate repetitive tasks, assist in image interpretation and quantification, and integrate imaging with other data types for predictive modeling.

  • Radiomics provides quantitative features from medical images which can reveal undetectable characteristics of diseases
  • AI manages high-dimensional medical imaging data for decision support, image interpretation, and predictive modeling

Application of Radiomics and AI in Human Healthcare

In human health care, radiomics and AI have found use in hospital management, as virtual healthcare assistants, for remote patient monitoring, and notably, medical diagnostics and imaging. The integration of these technologies into routine clinical workflows and diagnostics has aimed to enhance accuracy and reproducibility.

  • Radiomics and AI are used for hospital management and virtual assistants
  • They aid in remote patient monitoring
  • These technologies are crucial tools in medical diagnostics and imaging

Application of Radiomics and AI in Veterinary Diagnostic Imaging

Despite being somewhat behind rate of adoption as compared to human medicine, the field of veterinary medicine is increasingly recognizing the benefits of and incorporating radiomics and AI in diagnostic imaging. The paper provides an overview of the recent application instances of these technologies in veterinary diagnostic imaging.

  • There’s increasing action towards adopting radiomics and AI in the veterinary field
  • The paper gives an overview of current applications of these technologies in veterinary diagnostic imaging

Cite This Article

APA
Bouhali O, Bensmail H, Sheharyar A, David F, Johnson JP. (2022). A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging. Vet Sci, 9(11), 620. https://doi.org/10.3390/vetsci9110620

Publication

Veterinary sciences
ISSN: 2306-7381
NlmUniqueID: 101680127
Country: Switzerland
Language: English
Volume: 9
Issue: 11
PII: 620

Researcher Affiliations

Bouhali, Othmane
  • High Energy and Medical Physics Group, Department of Engineering, Education City, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar.
Bensmail, Halima
  • Qatar Computing Research Institute, Department of Sciences, Education City, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.
Sheharyar, Ali
  • High Energy and Medical Physics Group, Department of Engineering, Education City, Texas A&M University at Qatar, Doha P.O. Box 23874, Qatar.
David, Florent
  • Equine Veterinary Medical Center, A Member of Qatar Foundation, Al Shaqab Street, Al Rayyan, Doha P.O. Box 6788, Qatar.
Johnson, Jessica P
  • Equine Veterinary Medical Center, A Member of Qatar Foundation, Al Shaqab Street, Al Rayyan, Doha P.O. Box 6788, Qatar.

Conflict of Interest Statement

The authors declare no conflict of interest.

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