Artificial intelligence 101 for veterinary diagnostic imaging.
artificial intelligence
convolutional neural network
machine learning
natural language processing
veterinary radiologist
Journal
Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
ISSN: 1740-8261
Titre abrégé: Vet Radiol Ultrasound
Pays: England
ID NLM: 9209635
Informations de publication
Date de publication:
Dec 2022
Dec 2022
Historique:
revised:
18
01
2022
received:
09
08
2021
accepted:
08
02
2022
entrez:
14
12
2022
pubmed:
15
12
2022
medline:
16
12
2022
Statut:
ppublish
Résumé
The prevalence and pervasiveness of artificial intelligence (AI) with medical images in veterinary and human medicine is rapidly increasing. This article provides essential definitions of AI with medical images with a focus on veterinary radiology. Machine learning methods common in medical image analysis are compared, and a detailed description of convolutional neural networks commonly used in deep learning classification and regression models is provided. A brief introduction to natural language processing (NLP) and its utility in machine learning is also provided. NLP can economize the creation of "truth-data" needed when training AI systems for both diagnostic radiology and radiation oncology applications. The goal of this publication is to provide veterinarians, veterinary radiologists, and radiation oncologists the necessary background needed to understand and comprehend AI-focused research projects and publications.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
817-827Informations de copyright
© 2022 American College of Veterinary Radiology.
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