Artificial intelligence 101 for veterinary diagnostic imaging.

Journal: Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Published Date:

Abstract

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.

Authors

  • Adrien-Maxence Hespel
    Department of Small Animal Clinical Sciences, University of Tennessee, Knoxville, Tennessee, USA.
  • Youshan Zhang
    Department of Computer Science, Lehigh University, Bethlehem, PA, United States.
  • Parminder S Basran
    College of Veterinary Medicine, Cornell University, Ithaca, NY.