Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.3
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

A special issue of Veterinary Sciences (ISSN 2306-7381).

Deadline for manuscript submissions: closed (24 March 2026) | Viewed by 11132

Special Issue 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 “Artificial Intelligence, Machine Learning and Deep Learning in Veterinary Clinical Applications”. 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. Topics covered 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-blind 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 (7 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, Other

22 pages, 19524 KB  
Article
Clinical Spatial Distribution of Aquaporin-1 in Camel Cornea Using Assistive AI Applications
by Liana Fericean, Ahmed Magdy, Reda Rashed, Khaled Shoghy, Adel Abdelkhalek, Ahmed Abdeen, Banatean-Dunea Ioan, Mihaela Ostan, Olga Rada and Mohamed Abdo
Vet. Sci. 2026, 13(5), 425; https://doi.org/10.3390/vetsci13050425 - 27 Apr 2026
Viewed by 359
Abstract
The cornea of the dromedary camel is essential for maintaining ocular clarity and protecting the eye in dry, dusty, and thermally stressful environments. Aquaporins are membrane channels that facilitate water transport, and AQP1 has been widely implicated in corneal fluid homeostasis in several [...] Read more.
The cornea of the dromedary camel is essential for maintaining ocular clarity and protecting the eye in dry, dusty, and thermally stressful environments. Aquaporins are membrane channels that facilitate water transport, and AQP1 has been widely implicated in corneal fluid homeostasis in several species. The present work investigated, for the first time, the regional distribution of AQP1 in the camel cornea. Corneas collected from twelve healthy adult camels after slaughter were divided into nine anatomical regions: central (C), middle dorsal (MD), middle ventral (MV), middle nasal (MN), middle temporal (MT), peripheral dorsal (PD), peripheral ventral (PV), peripheral nasal (PN), and peripheral temporal (PT). Histological examination and immunohistochemistry were combined with digital morphometry to assess corneal layer thickness and AQP1 localization. AQP1 labeling was identified in the corneal epithelium, stromal keratocytes, and endothelium. Epithelial staining differed among regions and was most pronounced in the peripheral nasal region, whereas stromal keratocytes and endothelial cells showed strong and relatively uniform immunoreactivity. These findings indicate that AQP1 is broadly expressed in the camel cornea and likely contributes to regional control of hydration and tissue maintenance in an arid-adapted species. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications)
►▼ Show Figures

Figure 1

17 pages, 1451 KB  
Article
AI-Based Predictive Modelling and Alert Framework for Mortality Risk and Cost–Benefit Analysis in Rabbit Production
by Szilveszter Csorba, Erika Országh, Ákos Józwiák, Zoltán Német, Miklós Süth, Andrea Zentai and Zsuzsa Farkas
Vet. Sci. 2026, 13(4), 377; https://doi.org/10.3390/vetsci13040377 - 13 Apr 2026
Viewed by 569
Abstract
Mortality events in commercial rabbit production can lead to significant economic losses, highlighting the need for earlier identification of elevated mortality risk at the group level using routinely collected production data. This study presents a machine learning–based framework for predicting mortality risk at [...] Read more.
Mortality events in commercial rabbit production can lead to significant economic losses, highlighting the need for earlier identification of elevated mortality risk at the group level using routinely collected production data. This study presents a machine learning–based framework for predicting mortality risk at future observation points using routinely collected production data. Models were developed using group-level variables and evaluated with StratifiedGroupKFold cross-validation to prevent information leakage. The selected XGBoost model achieved a balanced performance, with a recall of 0.78 ± 0.03, precision of 0.59 ± 0.04, and ROC–AUC of 0.72 ± 0.02. Predictions were translated into an alert system based on a predefined threshold, prioritising sensitivity while maintaining a moderate false alert rate. A scenario-based cost–benefit analysis indicated that economic outcomes are highly dependent on intervention effectiveness, with positive returns observed under moderate to optimistic assumptions. Overall, the framework demonstrates the feasibility of integrating predictive modelling with alert-based decision support in rabbit production, although real-world validation under commercial farm conditions is required to confirm its practical effectiveness. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications)
►▼ Show Figures

Figure 1

12 pages, 1451 KB  
Article
Automated Detection of Parasitic Elements in Veterinary Fecal Samples Using a Deep Learning-Based Object Detection Framework
by Jing Yang, Bo Yang, Qingxiang You, Zhenqing Li and Yoshinori Yamaguchi
Vet. Sci. 2026, 13(3), 257; https://doi.org/10.3390/vetsci13030257 - 10 Mar 2026
Viewed by 632
Abstract
Parasitic infections in veterinary medicine are commonly diagnosed through microscopic examination of fecal samples, yet traditional manual methods are labor-intensive and subject to diagnostic variability. This study investigates YOLOv8 for automated identification of parasitic elements in fecal microscopy images. Six parasitic taxa were [...] Read more.
Parasitic infections in veterinary medicine are commonly diagnosed through microscopic examination of fecal samples, yet traditional manual methods are labor-intensive and subject to diagnostic variability. This study investigates YOLOv8 for automated identification of parasitic elements in fecal microscopy images. Six parasitic taxa were analyzed at 1000×, 2500×, and 10,000× magnifications: Spirometra eggs, Dipylidium egg packets, hookworm eggs, Ascaris eggs, Giardia cysts, and Trichomonas trophozoites. The dataset comprised 326 images with 3710 annotated objects, split at the sample level into training (70%), validation (15%), and testing (15%) sets. The YOLOv8n model achieved mean average precision (mAP@0.5) of 0.982 ± 0.015 across 5-fold cross-validation. Per-class AP exceeded 0.97 for five taxa, with Trichomonas achieving 0.952. Inference time averaged under 60 ms per image on a standard CPU. These results demonstrate that YOLOv8 provides accurate and efficient detection of diverse parasitic elements, supporting its potential as a clinical screening tool. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications)
►▼ Show Figures

Figure 1

20 pages, 3268 KB  
Article
Portable Electronic Olfactometer for Non-Invasive Screening of Canine Ehrlichiosis: A Proof-of-Concept Study Using Machine Learning
by Silvana Valentina Durán Cotrina, Cristhian Manuel Durán Acevedo and Jeniffer Katerine Carrillo Gómez
Vet. Sci. 2026, 13(1), 88; https://doi.org/10.3390/vetsci13010088 - 15 Jan 2026
Viewed by 659
Abstract
Canine ehrlichiosis, caused by Ehrlichia canis, represents a relevant challenge in veterinary medicine, particularly in resource-limited settings where access to laboratory-based diagnostics may be constrained. This pilot and exploratory study aimed to evaluate the feasibility of a portable electronic olfactometer as a [...] Read more.
Canine ehrlichiosis, caused by Ehrlichia canis, represents a relevant challenge in veterinary medicine, particularly in resource-limited settings where access to laboratory-based diagnostics may be constrained. This pilot and exploratory study aimed to evaluate the feasibility of a portable electronic olfactometer as a non-invasive screening approach, based on the analysis of volatile organic compounds (VOCs) present in breath, saliva, and hair samples from dogs. Signals were acquired using an array of eight metal-oxide (MOX) gas sensors (MQ and TGS series). After preprocessing, principal component analysis (PCA) was applied for dimensionality reduction, and the resulting features were analyzed using supervised machine-learning classifiers, including AdaBoost, support vector machines (SVM), k-nearest neighbors (k-NN), and Random Forests (RF). A total of 38 dogs (19 PCR-confirmed infected cases and 19 controls) were analyzed, generating 114 samples evenly distributed across the three biological matrices. Among the evaluated models, SVM showed the most consistent performance, particularly for saliva samples, achieving an accuracy, sensitivity, and precision of 94.7% (AUC = 0.964). In contrast, breath and hair samples showed lower discriminative performance. Given the limited sample size and the exploratory nature of the study, these results should be interpreted as preliminary; nevertheless, they suggest that electronic olfactometry may represent a complementary, low-cost, non-invasive screening tool for future research on canine ehrlichiosis, rather than a standalone diagnostic method. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications)
►▼ Show Figures

Graphical abstract

16 pages, 1193 KB  
Article
Classification of Clinical Outcomes in Hospitalized Asian Elephants Using Machine Learning and Survival Analysis: A Retrospective Study (2019–2024)
by Worapong Kosaruk, Veerasak Punyapornwithaya, Pichamon Ueangpaiboon and Taweepoke Angkawanish
Vet. Sci. 2025, 12(10), 998; https://doi.org/10.3390/vetsci12100998 - 16 Oct 2025
Viewed by 1529
Abstract
Captive Asian elephants (Elephas maximus) frequently present to hospitals with complex, multisystemic diseases, yet veterinarians lack objective tools to predict and classify clinical outcomes. Decision-making often relies on experience or anecdote, and few studies have applied data-driven approaches in wildlife medicine. [...] Read more.
Captive Asian elephants (Elephas maximus) frequently present to hospitals with complex, multisystemic diseases, yet veterinarians lack objective tools to predict and classify clinical outcomes. Decision-making often relies on experience or anecdote, and few studies have applied data-driven approaches in wildlife medicine. This study developed a machine learning–based classification model using routinely collected clinical data. A total of 467 medical records from hospitalized elephants at Thailand’s National Elephant Institute (2019–2024) were retrospectively analyzed. Four variables (age, sex, disease group, and length of stay [LOS]) were used to train four classification algorithms: Random Forest, eXtreme Gradient Boosting, Naïve Bayes, and multinomial logistic regression. The Random Forest model achieved the highest classification performance (accuracy = 86.3%; log-loss = 0.374), with disease group, LOS, and age as key predictors. Survival analysis revealed distinct hospitalization trajectories across disease groups: acute conditions like elephant endotheliotropic herpesvirus-hemorrhagic disease and toxicosis showed rapid early declines, whereas dental and renal cases followed more prolonged courses. Our findings demonstrate the preliminary feasibility of outcome classification in elephant care and highlight the potential of clinical data science to improve in-hospital prognostication, monitoring, and treatment planning in zoological and wildlife medicine. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

23 pages, 1791 KB  
Review
Artificial Intelligence in Veterinary Education: Preparing the Workforce for Clinical Applications in Diagnostics and Animal Health
by Esteban Pérez-García, Ana S. Ramírez, Miguel Ángel Quintana-Suárez, Magnolia M. Conde-Felipe, Conrado Carrascosa, Inmaculada Morales, Juan Alberto Corbera, Esther SanJuan and Jose Raduan Jaber
Vet. Sci. 2026, 13(2), 181; https://doi.org/10.3390/vetsci13020181 - 12 Feb 2026
Viewed by 1912
Abstract
Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is rapidly transforming clinical veterinary practice by enhancing diagnostics, disease surveillance and decision support processes across animal health domains. The safe and effective clinical deployment of these technologies, however, depends critically on [...] Read more.
Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is rapidly transforming clinical veterinary practice by enhancing diagnostics, disease surveillance and decision support processes across animal health domains. The safe and effective clinical deployment of these technologies, however, depends critically on the preparedness of the veterinary workforce, positioning veterinary education as a strategic enabler of translational adoption. This narrative review examines the integration of AI within veterinary education as a foundational step toward its responsible application in clinical practice. We synthesize current evidence on AI-driven tools relevant to veterinary curricula, including generative and multimodal large language models, intelligent tutoring systems, virtual and augmented reality platforms and AI-based decision support tools applied to imaging, epidemiology, parasitology, food safety and animal health. Particular attention is given to how the structured educational use of AI mirrors real-world clinical workflows and supports the development of competencies essential for clinical translation, such as data interpretation, uncertainty management, ethical reasoning and professional accountability. The review further addresses ethical, regulatory and cognitive considerations associated with AI adoption, including algorithmic bias, data privacy, equity of access and the risks of overreliance, emphasizing their direct implications for diagnostic reliability and animal welfare. By framing veterinary education as a controlled and reflective environment for AI engagement, this article highlights how pedagogically grounded training can facilitate safer clinical deployment, foster interdisciplinary collaboration and align technological innovation with professional standards in veterinary medicine. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

28 pages, 461 KB  
Systematic Review
Artificial Intelligence for the Diagnosis of Respiratory Diseases in Dogs and Cats: A Systematic Review
by Franklin Parrales-Bravo, Janio Jadán-Guerrero, Katherine Medina-Castro and Rosangela Caicedo-Quiroz
Vet. Sci. 2026, 13(2), 163; https://doi.org/10.3390/vetsci13020163 - 7 Feb 2026
Viewed by 1612
Abstract
Respiratory diseases represent a leading cause of veterinary consultations in dogs and cats, yet their detection remains challenging due to clinical variability and subjective interpretation of traditional diagnostic methods. In recent years, artificial intelligence (AI) has emerged as a promising tool to augment [...] Read more.
Respiratory diseases represent a leading cause of veterinary consultations in dogs and cats, yet their detection remains challenging due to clinical variability and subjective interpretation of traditional diagnostic methods. In recent years, artificial intelligence (AI) has emerged as a promising tool to augment veterinary diagnostics through automated analysis of imaging and physiological data. This systematic review synthesizes and critically evaluates 24 studies published from 2019 onward that explore AI applications to support the detection of respiratory diseases in dogs and cats, focusing on three complementary modalities: audio-based (e.g., respiratory sounds), image-based (e.g., chest radiographs), and multimodal approaches. Our findings indicate that deep learning models, particularly convolutional neural networks (CNNs) and transformer architectures, achieve clinically relevant accuracy in detecting conditions such as cardiomegaly, alveolar patterns, and Brachycephalic Obstructive Airway Syndrome (BOAS). However, significant barriers remain, including data scarcity, lack of standardized datasets, and limited real-world validation. This review highlights the transformative potential of AI in veterinary respiratory diagnostics while underscoring the need for collaborative efforts in data sharing, methodological standardization, and clinical integration to realize its full impact in practice. Full article
(This article belongs to the Special Issue AI, Deep Learning and Machine Learning in Veterinary Clinical Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop