This research topic delves into advancing diagnostic imaging techniques for veterinary medicine, combining medical imaging principles with experimental methodologies to address challenges unique to animal healthcare. With the rapid evolution of imaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and advanced radiographic methods, veterinary diagnostics stands at the forefront of innovation to improve patient care across diverse species.

The research encompasses several key areas: optimizing imaging protocols for non-conventional and exotic mammals, developing novel imaging biomarkers for disease detection, and integrating artificial intelligence (AI) for automated image analysis. A significant focus lies in translating advances from human diagnostic imaging to the veterinary field while adapting to species-specific anatomical and physiological variations. The intersection of medical imaging and veterinary experimental studies also opens pathways for interdisciplinary collaboration.

By leveraging AI and machine learning, the research aims to enhance diagnostic accuracy, reduce  examination times, and provide reproducible results, all while contributing to One Health initiatives hat link human, animal, and environmental health.

Five publications related to the Research Topic for the candidate interview:

1. Boissady E, de La Comble A, Zhu X, Hespel AM. Artificial intelligence evaluating primary thoracic lesions has an overall lower error rate compared to veterinarians or veterinarians in conjunction with the artificial intelligence. Vet Radiol Ultrasound. (2020) 61:619–27. DOI: https://doi.org/10.1111/vru.12912
2. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521: 436–444
3. Bruno MA, Walker EA, Abujudeh HH. Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. RadioGraphics. (2015) 35:1668–76. DOI: https://doi.org/10.1148/rg.2015150023
4. Topol, E.J., 2019. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. https://doi.org/10.1038/s41591-018-0300-7
5. Itri, JN, and Patel, SH. Heuristics and cognitive error in medical imaging. Am J Roentgenol. (2018) 210:1097–105. DOI: https://doi.org/10.2214/AJR.17.18907

Tutor: Prof. Tommaso Banzato
mail: tommaso.banzato@unipd.it