Artificial Intelligence in Veterinary Imaging: An Overview

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.

The potential application of artificial intelligence in veterinary clinical practice and biomedical research

“…Computer-enhanced visualization (CV) involves imaging and interpretation using ultrasonic machines. Units where CV is changing the game include image-based diagnosis and image-guided surgery ( 28 ). This CV-enhanced imaging has several applications spots in both human and veterinary medicine, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) scans, retinal photography, dermoscopy, and histology, among others.…”

“…A major bias that the application of AI and deep learning methods could remove is the subjectivity of image interpretation. Although several developments have been reported in the field of human medical image analysis over the past few decades ( 28 ), the use in veterinary clinical practice is still progressive, especially in low and middle-income regions of the world. Utilizing a blend of radionics and AI can be integrated into several medical imaging applications, including disease detection, characterization, and monitoring as seen in a study by Fraiwan and Abutarbush on predicting the need for surgery in horses presented with colic and the prognosis of those procedures ( 29 ).…”

"Artificial Intelligence in Veterinary Care: A Review of Applications for Animal Health

“…The accessibility and affordability of technology are opening up new opportunities for veterinarians to enhance the level of care they provide to animals [4]. One notable area where technology is expected to make a significant impact is in the realm of diagnostics [2,3]. With the advent of advanced imaging systems, veterinarians can now obtain a more comprehensive and detailed view of an animal's internal structures [20].…”

“…AI systems can evaluate enormous volumes of data to find patterns that might not be visible to the human eye [32]. By harnessing the power of machine learning algorithms, veterinarians can leverage AI technology to aid in the diagnosis of complex diseases [3]. This not only saves valuable time but also improves accuracy, leading to more effective treatment plans.…”

“…With its increasing capabilities and applications, AI has demonstrated immense potential in clinical examination, diagnosis, and treatment of animals [2]. Further, this essay aims to review the role of AI in Vet Med, highlighting its applications, benefits, limitations, and future prospects [3] The applications of AI in veterinary medicine are diverse and far-reaching, and one notable application is the development of AI-powered diagnostic systems [4]. These systems utilize machine learning algorithms to evaluate enormous volumes of data, such as medical records, lab results, and imaging studies, which aid in the diagnostic process by detecting patterns and identifying subtle anomalies, AI algorithms can assist veterinarians in making more accurate diagnoses [5].…”