Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.9
2.7
Journals
Veterinary Sciences
Special Issues
AI, Deep Learning and Machine Learning in Veterinary Clinical Applications:...
share announcement

AI, Deep Learning and Machine Learning in Veterinary Clinical Applications: 2nd Edition

A Special Issue of Veterinary Sciences (ISSN 2306-7381).

Deadline for manuscript submissions: 26 December 2026 | Viewed by 2201

Editors

Dr. Liana Fericean

E-Mail Website
Guest Editor
Department of Biology and Plant Protection, Faculty of Agricultural Sciences, University of Life Sciences King Michael I, 300645 Timisoara, Romania
Interests: comparative anatomy; disease resistance; cellular nanotechnology
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Abdo

E-Mail Website
Guest Editor
Department of Anatomy and Embryology, Badr University in Cairo (BUC), Badr University, Cairo 11829, Egypt
Interests: immunomodulation; therapeutic innovation; translational medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Abdeen

E-Mail Website
Guest Editor
Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, Benha University, Toukh 13736, Egypt
Interests: environmental contaminants; molecular toxicology; biomarker and drug target discovery; preventive medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, titled “AI, Deep Learning and Machine Learning in Veterinary Clinical Applications: 2nd Edition”. The integration of AI-driven technologies in veterinary medicine represents a rapidly evolving frontier with the potential to revolutionize animal healthcare. As veterinary professionals face increasingly complex diagnostic and therapeutic challenges, artificial intelligence, including machine learning and deep learning, offers powerful tools to enhance clinical decision-making, improve diagnostic accuracy, and personalize treatment strategies. This research area is of growing importance as it bridges veterinary science, data science, and computational medicine. This Special Issue aims to compile high-quality contributions focused on the application of AI, ML, and DL in clinical veterinary contexts. Submission topics should align with the journal’s focus on innovation and technological advancement in animal health, diagnostics, and disease management.

This Special Issue is aligned with the journal’s scope and seeks to foster cross-disciplinary collaborations between veterinarians, computer scientists, and bioengineers. Both original research articles and review papers are welcome. We look forward to receiving your valuable contributions and advancing this exciting and impactful field together.

Dr. Liana Fericean
Dr. Mohamed Abdo
Dr. Ahmed Abdeen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Veterinary Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • deep learning (DL)
  • veterinary diagnostics
  • disease prediction
  • medical imaging
  • animal health
  • monitoring clinical applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

19 pages, 1545 KB  
Article
Tumor-Specific Classification of Canine Cutaneous and Subcutaneous Tumors Using Heat-Diffusion Imaging and Artificial Intelligence
by Gillian Dank, Tali Buber, Liron Levy-Hirsh, Michael S. Kent, Uri Greenberg, Yael Harpaz and Erez Hanael
Vet. Sci. 2026, 13(9), 932; https://doi.org/10.3390/vetsci13090932 - 9 Sep 2026
Viewed by 380
Abstract
Introduction: Cutaneous and subcutaneous tumors are frequently encountered in canine patients, posing diagnostic challenges in general veterinary practice. Heat-Diffusion Imaging (HDI) is an active dynamic thermography technique that evaluates tissue thermal changes following controlled stimulation. Integration of HDI with artificial intelligence (AI) provides [...] Read more.
Introduction: Cutaneous and subcutaneous tumors are frequently encountered in canine patients, posing diagnostic challenges in general veterinary practice. Heat-Diffusion Imaging (HDI) is an active dynamic thermography technique that evaluates tissue thermal changes following controlled stimulation. Integration of HDI with artificial intelligence (AI) provides a non-invasive approach for tumor classification. Objective: Our objective was to develop and internally validate HDI-based, tumor-specific AI classifiers for differentiating canine mast cell tumors (MCTs) and lipomas from other cutaneous and subcutaneous masses. Methods: This study included 669 dogs with 1010 cutaneous and subcutaneous masses. Dynamic thermal data were recorded from each mass using an HDI system. This was followed by cytological or histopathological diagnosis. Thermal features were extracted from the recordings and used to develop, train, and internally validate a primary AI-based classifier that distinguishes benign from malignant lesions. A diagnostically confirmed subgroup was used to train and internally validate tumor-specific classifiers for lipomas and mast cell tumors. The diagnostic performance of all classifiers was independently evaluated. Results: The primary classifier cohort comprised 952 masses. Of these, 760 masses were assigned to the training cohort and 192 masses to the test cohort. The primary classifier achieved a sensitivity of 92% (95% CI 83.6–96.3) in the test set of 192 masses, and a prevalence-adjusted negative predictive value (NPV) of 98.1% at an assumed malignancy prevalence of 15%. Among the 55 masses assessed by the lipoma classifier, sensitivity was 86.4% (95% CI 73.3–93.6) and specificity 100% (95% CI 74.1–100); all 38 masses flagged as lipoma were lipomas. Among the 81 masses assessed by the MCT classifier, sensitivity was 31.8% (95% CI 20.0–46.6) and specificity 94.6% (95% CI 82.3–98.5); all 16 masses flagged as MCT were malignant, and 14/16 were diagnosed as MCTs. Conclusions: These findings demonstrate that combining HDI-derived thermal features with AI enables promising, non-invasive differentiation of common canine cutaneous tumor types. The two-tiered diagnostic approach improves upon the primary classifier by providing tumor-specific classification. This strategy may be particularly valuable in general practice settings, where access to immediate cytological or histopathological evaluation can be limited. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications: 2nd Edition)
►▼ Show Figures

Figure 1

Review

Jump to: Research

50 pages, 3308 KB  
Review
qEEG and Functional Connectivity as a Translational Bridge Between Humans and Dogs in Epilepsy and Associated Disorders: From Spontaneous Model to Automatic Classification—An Integrative Review
by Dan Anghelinici and Mihai Musteata
Vet. Sci. 2026, 13(8), 803; https://doi.org/10.3390/vetsci13080803 - 14 Aug 2026
Viewed by 674
Abstract
Quantitative electroencephalography (qEEG) converts the raw EEG signal into reproducible numerical descriptors (spectral power, hemispheric symmetry, coherence and signal complexity) and has emerged as a candidate translational biomarker linking human and canine neurology. This integrative review examined the diagnostic, prognostic, pharmacological and translational [...] Read more.
Quantitative electroencephalography (qEEG) converts the raw EEG signal into reproducible numerical descriptors (spectral power, hemispheric symmetry, coherence and signal complexity) and has emerged as a candidate translational biomarker linking human and canine neurology. This integrative review examined the diagnostic, prognostic, pharmacological and translational value of qEEG, with emphasis on functional connectivity, and assessed the comparability of the dog as a natural model of human disease. Seventy-five studies were included, spanning epilepsy, acute brain injury, neurodegeneration, rehabilitation and paroxysmal disorders. In both species, epilepsy was consistently associated with altered spectral power and with reduced or reorganized coherence, and interictal abnormalities were demonstrable even in the absence of visible epileptiform discharges. Dogs reproduced the human patterns closely: phenobarbital induced the spectral redistribution predicted by human pharmaco-EEG data, canine cognitive dysfunction reproduced the slowing and the sleep-architecture changes described in Alzheimer’s disease, and a single machine-learning pipeline classified human and canine recordings with comparable accuracy. Conversely, acute brain injury remains virtually unexplored in the dog, and no canine normative database comparable to the human ones is yet available. The evidence was limited by heterogeneous acquisition protocols, small samples and scarce longitudinal veterinary data. qEEG, and coherence in particular, appears to be a promising cross-species biomarker of network dysfunction and supports the dog as a translational platform, although standardized validation remains necessary. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop