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Animals
Special Issues
Artificial Intelligence Applications for Veterinary Medicine
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Artificial Intelligence Applications for Veterinary Medicine

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Veterinary Clinical Studies".

Deadline for manuscript submissions: 1 October 2026 | Viewed by 3921

Special Issue Editor

Dr. Sofia Alves-Pimenta

E-Mail Website
Guest Editor
1. Veterinary and Animal Science Research Centre (CECAV), Vila Real, Portugal
2. Department of Animal Science, University of Trás-os-Montes and Alto Douro (UTAD), Vila Real, Portugal
3. Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), Vila Real, Portugal
Interests: clinical anatomy; applied morphology; osteoarthritis; pain management; software development for analysis of medical imaging; screening heritable diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has emerged as a transformative tool in veterinary medicine, offering innovative solutions across diagnostics, treatment planning, disease surveillance, and animal health management. Machine learning algorithms, image recognition, and predictive analytics are increasingly applied to enhance diagnostic accuracy, optimize therapeutic interventions, and streamline clinical decision-making. AI tools are being integrated into imaging modalities such as radiography and ultrasound, enabling automated lesion detection and classification. Furthermore, natural language processing is facilitating the extraction of clinical insights from unstructured veterinary records, contributing to evidence-based practice.

The aim of this Special Issue is to bring together the latest findings concerning the role of AI in veterinary science and animal healthcare. Original research papers and literature reviews from different research areas, such as diagnostic imaging and analysis, pathology, epidemiology, animal behavior, telemedicine, veterinary robotics, pet health monitoring devices, personalized treatment plans, supply chain and inventory management, veterinary drug discovery, and chatbots for pet owners, are encouraged. These contributions will illustrate how AI is being employed to solve complex veterinary challenges, ranging from diagnostic automation to disease prevention strategies. Additional topics and interdisciplinary studies regarding the ethical, regulatory, and economic aspects of AI adoption in veterinary practice will also be considered.

Dr. Sofia Alves-Pimenta
Guest Editor

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. Animals is an international peer-reviewed open access semimonthly 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 2400 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

  • diagnostic imaging
  • disease surveillance and outbreak prediction
  • predictive analytics for disease
  • clinical decision support systems

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Published Papers (3 papers)

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Research

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22 pages, 5375 KB  
Article
A Novel AAF-SwinT Model for Automatic Recognition of Abnormal Goat Lung Sounds
by Shengli Kou, Decao Zhang, Jiadong Yu, Yanling Yin, Weizheng Shen and Qiutong Cen
Animals 2026, 16(7), 1021; https://doi.org/10.3390/ani16071021 - 26 Mar 2026
Viewed by 379
Abstract
In abnormal goat lung sound recognition, high inter-class similarity and large intra-class variability pose significant challenges. To address this issue and improve recognition performance, we propose a deep learning model, AAF-SwinT, based on an improved Swin Transformer. The model replaces the original Swin [...] Read more.
In abnormal goat lung sound recognition, high inter-class similarity and large intra-class variability pose significant challenges. To address this issue and improve recognition performance, we propose a deep learning model, AAF-SwinT, based on an improved Swin Transformer. The model replaces the original Swin Transformer self-attention module with Axial Decomposed Attention (ADA), modeling the temporal and frequency axes separately and integrating attention weights to mitigate inter-class feature similarity. Adaptive Spatial Aggregation for Patch Merging (ASAP) is designed to emphasize key time-frequency regions, and a Frequency-Aware Multi-Layer Perceptron (FAM) is introduced to model features across different frequency bands, further enhancing the discriminative ability for abnormal lung sounds. Experiments on a self-constructed goat lung sound dataset demonstrate that AAF-SwinT achieves an accuracy of 88.21%, outperforming existing mainstream Transformer-based models by 2.68–5.98%. Ablation studies further confirm the effectiveness of each proposed module, improving the accuracy of baseline Swin Transformer model from 85.53% to 88.21%. These results indicate that the proposed approach exhibits strong robustness and practical potential for abnormal lung sound recognition in goats, providing technical support for early diagnosis and management of respiratory diseases in large-scale goat farming. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Veterinary Medicine)
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14 pages, 1051 KB  
Communication
Development of an Explainable Machine Learning Computational Model for the Prediction of Severe Complications After Orchiectomy in Stallions
by Panagiota Tyrnenopoulou, Dimitris Kalatzis, Yiannis Kiouvrekis, Eugenia Flouraki, Leonidas Folias, Epameinondas Loukopoulos, Alexandros Starras, Panagiotis Chalvatzis, Vassiliki Tsioli, Vasia S. Mavrogianni and George C. Fthenakis
Animals 2026, 16(3), 377; https://doi.org/10.3390/ani16030377 - 25 Jan 2026
Viewed by 430
Abstract
The objective of the present study was to apply supervised Machine Learning to predict severe complications after equine orchiectomy. A dataset of 612 cases of orchiectomies in stallions was used for the development of a computational model, among which in 8.5% of cases [...] Read more.
The objective of the present study was to apply supervised Machine Learning to predict severe complications after equine orchiectomy. A dataset of 612 cases of orchiectomies in stallions was used for the development of a computational model, among which in 8.5% of cases severe complications (colic, continued stallion-like behaviour, evisceration, funiculitis, haemorrhage, and scrotal infection) were diagnosed post-orchiectomy. Three supervised Machine Learning tools were employed: Logistic Regression (12 different models evaluated), Random Forest (64 models), and Gradient Boosting (8 models). For the prediction of the development of severe complications post-orchiectomy, Logistic Regression was the tool that produced the best discrimination measures, where accuracy, precision and recall were 0.9134, 0.8391, and 0.9133, respectively. The results of the analysis for SHapley Additive exPlanations values for the impact of the independent variables in the prediction of the development of complications indicated that (a) the age of the horse and (b) the surgical technique employed were the two variables that mostly influenced the prediction outcome, findings that were unambiguous in the models developed by any Machine Learning tool. The findings of this study indicate that computational models could be used as adjunct tools to support clinical decisions in the peri-operative management of horses. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Veterinary Medicine)
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Review

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16 pages, 1827 KB  
Review
Disease Prediction in Cattle: A Mixed-Methods Review of Predictive Modeling Studies
by Lilli Heinen, Robert L. Larson and Brad J. White
Animals 2025, 15(17), 2481; https://doi.org/10.3390/ani15172481 - 23 Aug 2025
Viewed by 1273
Abstract
Predictive models use historical data to predict a future event and can be applied to a wide variety of tasks. A broader evaluation of the cattle literature is required to better understand predictive model performance across various health challenges and to understand data [...] Read more.
Predictive models use historical data to predict a future event and can be applied to a wide variety of tasks. A broader evaluation of the cattle literature is required to better understand predictive model performance across various health challenges and to understand data types utilized to train models. This narrative review aims to describe predictive model performance in greater detail across various disease outcomes, input data types, and algorithms with a specific focus on accuracy, sensitivity, specificity, and positive and negative predictive values. A secondary goal is to address important areas for consideration for future work in the beef cattle sector. In total, 19 articles were included. Broad categories of disease were covered, including respiratory disease, bovine tuberculosis, and others. Various input data types were reported, including demographic data, images, and laboratory test results, among others. Several algorithms were utilized, including neural networks, linear models, and others. Accuracy, sensitivity, and specificity values ranged widely across disease outcome and algorithm categories. Negative predictive values were greater than positive predictive values for most disease outcomes. This review highlights the importance of utilizing several performance metrics and concludes that future work should address prevalence of outcomes and class-imbalanced data. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications for Veterinary Medicine)
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