Artificial Intelligence in Veterinary Imaging: An Overview

Author:

Pereira Ana Inês1,Franco-Gonçalo Pedro123,Leite Pedro4,Ribeiro Alexandrine4,Alves-Pimenta Maria Sofia235ORCID,Colaço Bruno235ORCID,Loureiro Cátia67,Gonçalves Lio678ORCID,Filipe Vítor678ORCID,Ginja Mário123ORCID

Affiliation:

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

2. Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal

3. Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), 5000-801 Vila Real, Portugal

4. Neadvance Machine Vision SA, 4705-002 Braga, Portugal

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

6. School of Science and Technology, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal

7. Department of Engineering, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal

8. Institute for Systems and Computer Engineering (INESC-TEC), Technology and Science, 4200-465 Porto, Portugal

Abstract

Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.

Funder

Dys4Vet

European Regional Development Fund (ERDF) through COMPETE2020-The Operational Program for Competitiveness and Internationalization

Publisher

MDPI AG

Subject

General Veterinary

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