Artificial intelligence in veterinary diagnostic imaging: A literature review. (5th December 2022)

• Record Type: Journal Article
• Authors: Hennessey, Erin; DiFazio, Matthew; Hennessey, Ryan; Cassel, Nicky
• Is Part Of: Veterinary radiology & ultrasound. Volume 63(2022)supplement 1
• Page Start: 851
• Page End: 870
• Publication Date: 2022-12-05
• Subjects: computer vision -- machine learning -- neural network -- quantitative analysis -- radiology -- texture analysis; Veterinary radiology -- Periodicals; Radiologie vétérinaire -- Périodiques; Échographie vétérinaire -- Périodiques; Radiography -- Periodicals; Ultrasonography -- Periodicals; Diagnosis -- Periodicals; Veterinary Medicine -- Periodicals; Societies, Medical -- Periodicals; 636.089607
• DOI: 10.1111/vru.13163
• Languages: English
• ISSNs: 1058-8183

Abstract

Abstract: Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imaging. This review summarizes the small, yet growing body of artificial intelligence literature in veterinary imaging, provides necessary background to understand these papers, and provides author commentary on the state of the field. To date, less than 40 peer‐reviewed publications have utilized machine learning to perform imaging‐associated tasks across multiple anatomic regions in veterinary clinical and biomedical research. Major challenges in this field include collection and cleaning of sufficient image data, selection of high‐quality ground truth labels, formation of relationships between veterinary and machine learning professionals, and closure of the gap between academic uses of artificial intelligence and currently available commercial products. Further development of artificial intelligence has the potential to help meet the growing need for radiological services through applications in workflow, quality control, and image interpretation for both general practitioners and radiologists.