AI, Deep Learning, and Machine Learning in Veterinary Science Imaging

Topic Information

Dear Colleagues,

The topic on "AI, Deep Learning, and Machine Learning in Veterinary Science Imaging" delves into the transformative impact of artificial intelligence and advanced machine learning techniques on diagnostics and imaging in veterinary sciences. This edition explores how AI-driven tools are enhancing the accuracy, speed, and efficiency of diagnostic imaging in veterinary medicine, including applications in radiology, ultrasound, MRI, and CT scans. By integrating deep learning algorithms, veterinary professionals can now detect and diagnose conditions with unprecedented precision, enabling early intervention and personalized treatment plans for companion animals. The issue also covers the challenges and ethical considerations of implementing AI in veterinary practices, as well as the potential for AI to bridge gaps in access to specialized imaging services in remote areas. Through a collection of research articles, case studies, and expert opinions, this special edition serves as an essential resource for veterinarians, researchers, and engineers who are at the forefront of integrating AI into veterinary science.

Dr. Vitor Filipe
Dr. Lio Gonçalves
Dr. Mário Ginja
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Animals animals	2.7	4.9	2011	16.1 Days	CHF 2400	Submit
Computers computers	2.6	5.4	2012	15.5 Days	CHF 1800	Submit
Information information	2.4	6.9	2010	16.4 Days	CHF 1600	Submit
Journal of Imaging jimaging	2.7	5.9	2015	18.3 Days	CHF 1800	Submit
Veterinary Sciences vetsci	2.0	2.9	2014	21.2 Days	CHF 2100	Submit

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Published Papers (6 papers)

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All Animals Computers Information J. Imaging Veterinary Sciences

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14 pages, 568 KiB  

Open AccessReview

Artificial Intelligence in Chest Radiography—A Comparative Review of Human and Veterinary Medicine

by Andrea Rubini, Roberto Di Via, Vito Paolo Pastore, Francesca Del Signore, Martina Rosto, Andrea De Bonis, Francesca Odone and Massimo Vignoli

Vet. Sci. 2025, 12(5), 404; https://doi.org/10.3390/vetsci12050404 - 25 Apr 2025

Viewed by 216

Abstract

The integration of artificial intelligence (AI) into chest radiography (CXR) has greatly impacted both human and veterinary medicine, enhancing diagnostic speed, accuracy, and efficiency. In human medicine, AI has been extensively studied, improving the identification of thoracic abnormalities, diagnostic precision in emergencies, and [...] Read more.

The integration of artificial intelligence (AI) into chest radiography (CXR) has greatly impacted both human and veterinary medicine, enhancing diagnostic speed, accuracy, and efficiency. In human medicine, AI has been extensively studied, improving the identification of thoracic abnormalities, diagnostic precision in emergencies, and the classification of complex conditions such as tuberculosis, pneumonia, and COVID-19. Deep learning-based models assist radiologists by detecting patterns, generating probability maps, and predicting outcomes like heart failure. However, AI is still supplementary to clinical expertise due to challenges such as data limitations, algorithmic biases, and the need for extensive validation. Ethical concerns and regulatory constraints also hinder full implementation. In veterinary medicine, AI is still in its early stages and is rarely used; however, it has the potential to become a valuable tool for supporting radiologists in the future. However, challenges include smaller datasets, breed variability, and limited research. Addressing these through focused research on species with less phenotypic variability (like cats) and cross-sector collaborations could advance AI in veterinary medicine. Both fields demonstrate AI’s potential to enhance diagnostics but emphasize the ongoing need for human expertise in clinical decision making. Differences in anatomy structure between the two fields must be considered for effective AI adaptation. Full article

(This article belongs to the Topic AI, Deep Learning, and Machine Learning in Veterinary Science Imaging)

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23 pages, 6490 KiB  

Open AccessArticle

GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity

by Zhaoyang Yin, Zehua Wang, Junhua Ye, Suyin Zhou and Aijun Xu

Animals 2025, 15(7), 1040; https://doi.org/10.3390/ani15071040 - 3 Apr 2025

Viewed by 219

Abstract

Pig tracking contributes to the assessment of pig behaviour and health. However, pig tracking on real farms is very difficult. Owing to incomplete camera field of view (FOV), pigs frequently entering and exiting the camera FOV affect the tracking accuracy. To improve pig-tracking [...] Read more.

Pig tracking contributes to the assessment of pig behaviour and health. However, pig tracking on real farms is very difficult. Owing to incomplete camera field of view (FOV), pigs frequently entering and exiting the camera FOV affect the tracking accuracy. To improve pig-tracking efficiency, we propose a pig-tracking method that is based on skeleton feature similarity, which we named GcnTrack. We used YOLOv7-Pose to extract pig skeleton key points and design a dual-tracking strategy. This strategy combines IOU matching and skeleton keypoint-based graph convolutional reidentification (Re-ID) algorithms to track pigs continuously, even when pigs return from outside the FOV. Three identical FOV sets of data that separately included long, medium, and short duration videos were used to test the model and verify its performance. The GcnTrack method achieved a Multiple Object Tracking Accuracy (MOTA) of 84.98% and an identification F1 Score (IDF1) of 82.22% for the first set of videos (short duration, 87 s to 220 s). The tracking precision was 74% for the second set of videos (medium duration, average 302 s). The pigs entered the scene 15.29 times on average, with an average of 6.28 identity switches (IDSs) per pig during the tracking experiments on the third batch set of videos (long duration, 14 min). In conclusion, our method contributes an accurate and reliable pig-tracking solution applied to scenarios with incomplete camera FOV. Full article

(This article belongs to the Topic AI, Deep Learning, and Machine Learning in Veterinary Science Imaging)

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21 pages, 107660 KiB  

Open AccessArticle

YOLOv8A-SD: A Segmentation-Detection Algorithm for Overlooking Scenes in Pig Farms

by Yiran Liao, Yipeng Qiu, Bo Liu, Yibin Qin, Yuchao Wang, Zhijun Wu, Lijia Xu and Ao Feng

Animals 2025, 15(7), 1000; https://doi.org/10.3390/ani15071000 - 30 Mar 2025

Viewed by 300

Abstract

A refined YOLOv8A-SD model is introduced to address pig detection challenges in aerial surveillance of pig farms. The model incorporates the ADown attention mechanism and a dual-task strategy combining detection and segmentation tasks. Testing was conducted using top-view footage from a large-scale pig [...] Read more.

A refined YOLOv8A-SD model is introduced to address pig detection challenges in aerial surveillance of pig farms. The model incorporates the ADown attention mechanism and a dual-task strategy combining detection and segmentation tasks. Testing was conducted using top-view footage from a large-scale pig farm in Sichuan, with 924 images for detection training, 216 for validation, and 2985 images for segmentation training, with 1512 for validation. The model achieved 96.1% Precision and 96.3% mAP50 in detection tasks while maintaining strong segmentation performance (IoU: 83.1%). A key finding reveals that training with original images while applying segmentation preprocessing during testing provides optimal results, achieving exceptional counting accuracy (25.05 vs. actual 25.09 pigs) and simplifying practical deployment. The research demonstrates YOLOv8A-SD’s effectiveness in complex farming environments, providing reliable monitoring capabilities for intelligent farm management applications. Full article

(This article belongs to the Topic AI, Deep Learning, and Machine Learning in Veterinary Science Imaging)

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12 pages, 2088 KiB  

Open AccessArticle

Clinical Application of Monitoring Vital Signs in Dogs Through Ballistocardiography (BCG)

by Bolortuya Chuluunbaatar, YungAn Sun, Kyerim Chang, HoYoung Kwak, Jinwook Chang, WooJin Song and YoungMin Yun

Vet. Sci. 2025, 12(4), 301; https://doi.org/10.3390/vetsci12040301 - 24 Mar 2025

Viewed by 759

Abstract

This study evaluated the application of the BCG Sense1 wearable device for monitoring the heart rate (HR) and the respiratory rate (RR) in dogs, comparing its performance to the gold standard ECG under awake and anesthetized conditions. Data were collected from twelve dogs, [...] Read more.

This study evaluated the application of the BCG Sense1 wearable device for monitoring the heart rate (HR) and the respiratory rate (RR) in dogs, comparing its performance to the gold standard ECG under awake and anesthetized conditions. Data were collected from twelve dogs, with six awake beagles and six anesthetized client-owned dogs. Bland–Altman analysis and linear regression revealed strong correlations between BCG and ECG under both awake and anesthetized conditions (HR: r = 0.97, R² = 0.94; RR: r = 0.78, R² = 0.61, and p < 0.001). While slight irregularities were noted in respiratory rate measurements in both groups, potentially affecting the concordance between methods, BCG maintained a significant correlation with ECG under anesthesia (HR: r = 0.96, R² = 0.92; RR: r = 0.85, R² = 0.72, and p < 0.01). The wearable BCG-Sense 1 sensor enables continuous monitoring over 24 h, while ECG serves as the gold standard reference. These findings prove that BCG can be a good alternative to ECG for the monitoring of vital signs in clinical, perioperative, intraoperative, and postoperative settings. The strong correlation between the BCG and ECG signals in awake and anesthetized states highlights the prospects of BCG technology as a revolutionary method in veterinary medicine. As a non-invasive and real-time monitoring system, the BCG Sense1 device strengthens clinical diagnosis and reduces physiological variations induced by stress. Full article

(This article belongs to the Topic AI, Deep Learning, and Machine Learning in Veterinary Science Imaging)

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27 pages, 8269 KiB  

Open AccessArticle

Evaluating Optimal Deep Learning Models for Freshness Assessment of Silver Barb Through Technique for Order Preference by Similarity to Ideal Solution with Linear Programming

by Atchara Choompol, Sarayut Gonwirat, Narong Wichapa, Anucha Sriburum, Sarayut Thitapars, Thanakorn Yarnguy, Noppakun Thongmual, Waraporn Warorot, Kiatipong Charoenjit and Ronnachai Sangmuenmao

Computers 2025, 14(3), 105; https://doi.org/10.3390/computers14030105 - 16 Mar 2025

Viewed by 295

Abstract

Automating fish freshness assessment is crucial for ensuring quality control and operational efficiency in large-scale fish processing. This study evaluates deep learning models for classifying the freshness of Barbonymus gonionotus (Silver Barb) and optimizing their deployment in an automated fish quality sorting system. [...] Read more.

Automating fish freshness assessment is crucial for ensuring quality control and operational efficiency in large-scale fish processing. This study evaluates deep learning models for classifying the freshness of Barbonymus gonionotus (Silver Barb) and optimizing their deployment in an automated fish quality sorting system. Three lightweight deep learning architectures, MobileNetV2, MobileNetV3, and EfficientNet Lite2, were analyzed across 18 different configurations, varying model size (Small, Medium, Large) and preprocessing methods (With and Without Preprocessing). A dataset comprising 1200 images, categorized into three freshness levels, was collected from the Lam Pao Dam in Thailand. To enhance classification performance, You Only Look Once version 8 (YOLOv8) was utilized for object detection and image preprocessing. The models were evaluated based on classification accuracy, inference speed, and computational efficiency, with Technique for Order Preference by Similarity to Ideal Solution with Linear Programming (TOPSIS-LP) applied as a multi-criteria decision-making approach. The results indicated that the MobileNetV3 model with a large parameter size and preprocessing (M2-PL-P) achieved the highest closeness coefficient (CC) score, with an accuracy of 98.33% and an inference speed of 6.95 frames per second (fps). This study establishes a structured framework for integrating AI-driven fish quality assessment into fishery-based community enterprises, improving productivity and reducing reliance on manual sorting processes. Full article

(This article belongs to the Topic AI, Deep Learning, and Machine Learning in Veterinary Science Imaging)

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14 pages, 19850 KiB  

Open AccessArticle

Intelligent Deep Learning and Keypoint Tracking-Based Detection of Lameness in Dairy Cows

by Zongwei Jia, Yingjie Zhao, Xuanyu Mu, Dongjie Liu, Zhen Wang, Jiangtan Yao and Xuhui Yang

Vet. Sci. 2025, 12(3), 218; https://doi.org/10.3390/vetsci12030218 - 2 Mar 2025

Cited by 1 | Viewed by 699

Abstract

With the ongoing development of computer vision technologies, the automation of lameness detection in dairy cows urgently requires improvement. To address the challenges of detection difficulties and technological limitations, this paper proposes an automated scoring method for cow lameness that integrates deep learning [...] Read more.

With the ongoing development of computer vision technologies, the automation of lameness detection in dairy cows urgently requires improvement. To address the challenges of detection difficulties and technological limitations, this paper proposes an automated scoring method for cow lameness that integrates deep learning with keypoint tracking. First, the DeepLabCut tool is used to efficiently extract keypoint features during the walking process of dairy cows, which enables the automated monitoring and output of positional information. Then, the extracted positional data are combined with temporal data to construct a scoring model for cow lameness. The experimental results demonstrate that the proposed method tracks the keypoint of cow movement accurately in visible-light videos and satisfies the requirements for real-time detection. The model classifies the walking states of the cows into four levels, i.e., normal, mild, moderate, and severe lameness (corresponding to scores of 0, 1, 2, and 3, respectively). The detection results obtained in real-world real environments exhibit the high extraction accuracy of the keypoint positional information, with an average error of only 4.679 pixels and an overall accuracy of 90.21%. The detection accuracy for normal cows was 89.0%, with 85.3% for mild lameness, 92.6% for moderate lameness, and 100.0% for severe lameness. These results demonstrate that the application of keypoint detection technology for the automated scoring of lameness provides an effective solution for intelligent dairy management. Full article

(This article belongs to the Topic AI, Deep Learning, and Machine Learning in Veterinary Science Imaging)

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