Digital technologies, machine learning, and artificial intelligence in veterinary pathology PRIORITY RESEARCH GRANT CALL 2024

Digital technology, increasingly integrated into daily life, is revolutionizing the landscape of Veterinary Pathology. Innovative tools such as telemedicine, artificial intelligence in diagnostic techniques, drones for monitoring, virtual reality in education, and distance learning are transforming research, teaching, and diagnostic methods in this field. These technologies applied in Veterinary Pathology, not only advance animal care and welfare, but also significantly impact public health, for instance in monitoring zoonotic diseases and food safety, and improve the timing and accuracy of diagnoses. Ongoing technological progress necessitates continuous updating and careful selection of the most effective tools for each specific application, stimulating dynamic research involving both technology users and developers. Our team brings extensive experience in digital histological and cytological diagnostics, as well as teaching, developing virtual reality for Veterinary Medicine education, innovative techniques for wild animal health monitoring using drones, employing artificial intelligence and 3D imaging in the study of lesser-known species and their pathology. The PhD student will be then involved in studying, application, and eventually development of new digital tools with the aim to improve the accuracy and the turnaround time in diagnostic as well as teaching effectiveness.

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

  1. Al-Janabi S, Huisman A, Nap M, Clarijs R, van Diest PJ. Whole slide images as a platform for initial diagnostics in histopathology in a medium-sized routine laboratory. J Clin Pathol. 2012 Dec;65(12):1107-11. doi: 10.1136/jclinpath-2012-200878. Epub 2012 Oct 23. PMID: 23093511.
  2. Aubreville M, Bertram CA, Marzahl C, Gurtner C, Dettwiler M, Schmidt A, Bartenschlager F, Merz S, Fragoso M, Kershaw O, Klopfleisch R, Maier A. Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region. Sci Rep. 2020 Oct 5;10(1):16447. doi: 10.1038/s41598-020-73246-2. PMID: 33020510; PMCID: PMC7536430.
  3. Zuraw A, Aeffner F. Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review. Vet Pathol. 2022 Jan;59(1):6-25. doi: 10.1177/03009858211040484. Epub 2021 Sep 14. PMID: 34521285.
  4. Hubbard-Perez M, Luchian A, Milford C, Ressel L. Use of deep learning for the classification of hyperplastic lymph node and common subtypes of canine lymphomas: a preliminary study. Front Vet Sci. 2024 Jan 12;10:1309877. doi: 10.3389/fvets.2023.1309877. PMID: 38283371; PMCID: PMC10811236.
  5. Bollig N, Clarke L, Elsmo E, Craven M. Machine learning for syndromic surveillance using veterinary necropsy reports. PLoS One. 2020 Feb 5;15(2):e0228105. doi: 10.1371/journal.pone.0228105. PMID: 32023271; PMCID: PMC7001958.

Contact person

Dott.ssa Silvia Ferro

Department of Comparative Biomedicine and Food Science (https://www.bca.unipd.it/)
University of Padova
tel.: +39-049-8272872
e-mail: silvia.ferro@unipd.it