Omneity® Pellets
All-In-One Vitamin & Mineral Pellet
Topic:Artificial Intelligence
Artificial Intelligence (AI) in the equine industry involves the application of machine learning algorithms, computer vision, and data analytics to improve various aspects of horse care and management. AI technologies are used to analyze large datasets derived from equine health records, behavioral observations, and performance metrics, offering insights into health monitoring, disease prediction, and training optimization. Applications include automated lameness detection, gait analysis, and the prediction of injuries or illnesses based on historical data patterns. This page compiles peer-reviewed research studies and scholarly articles that explore the integration of AI technologies in equine science, focusing on their implementation, effectiveness, and potential impact on horse welfare and management.
Niacin Improves Cryopreserved Equine Sperm Quality and Gene Expression: An Artificial Intelligence Assisted Evaluation. Niacin acts as an antioxidant that protects cells from oxidative damage. This study evaluated the effects of adding niacin to the equine semen freezing extender on sperm quality and gene expression after cryopreservation. Ejaculates from ten stallions were frozen using the INRA 96 extender (control) or extenders supplemented with 10- and 20-mM niacin. After thawing, sperm were analysed for motility, kinematics, viability, membrane integrity, mitochondrial potential, lipid peroxidation, nitrite, hydrogen peroxide, malondialdehyde and reactive oxygen species (ROS) concentrations, DNA integrity, ...
Measuring emotional contagion in Horse-Human interactions: A systematic scoping review of methods and outcomes. Emotional contagion between horses and humans is a key aspect of their interaction, influencing welfare, training, and therapy, yet current methods for measuring this phenomenon lack standardization and consistency. Objective: The aim of this study was to verify what methods are used to assess emotional contagion in horse-human interactions, and what outcomes have been reported. Methods: A systematic search of Google Scholar, Web of Science, Scopus, PubMed, and Science Direct was conducted up to March 2024. Peer-reviewed studies assessing emotional transfer through behavioral and/or physiologi...
Agreement of the performance of equine electrocardiogram recording devices for ECG complexity analysis. Non-linear equine electrocardiography (ECG) analysis is an actively developing study area which has the potential to lead to novel, artificial intelligence-based diagnostic tools in equine cardiology. As more ECG recording devices are becoming available, there is a need to ensure results are interchangeable regardless of the equipment used to record the equine ECG. Objective: To evaluate the agreement of ECG complexity values obtained using the Televet™ and Equimetre™ systems. Methods: Cross-sectional clinical. Methods: ECGs were recorded using two devices simultaneously from 37 healthy Th...
Use of Artificial Intelligence to Detect Cardiac Rhythm Disturbances in Athletes: A Scoping Review. Artificial intelligence (AI) is increasingly used to enhance electrocardiogram (ECG) interpretation in human medicine. In equine athletes, exercise-associated arrhythmias are common and linked to sudden cardiac death at rates higher than in humans. However, ECG interpretation in horses remains time-consuming and subjective, with the clinical relevance of mild rhythm disturbances often unclear. Objective: Evaluate the application of AI to ECG interpretation for arrhythmia detection, with emphasis on current and potential use in athletic species, particularly horses. Methods: About 17 studies we...
Artificial intelligence tools to assess different levels of activity performed by semi-wild horses in grassland ecosystems. In order to understand the role of horses in ecosystems and to effectively use their grazing in the protection of grasslands, it is important to assess where they primarily stay, followed by whether these habitats are used for grazing or resting. The main goal of the study was the model development based on artificial intelligence tools which allow to distinguish the basic levels of activity performed by horses using data from an accelerometer mounted in a collar worn by animals. The model calibration was based on direct observations of five randomly selected Polish primitive horse mares. In o...
An iterative approach to identify key predictive features of fear reactivity and fearfulness in horses (Equus caballus). This study extends previous findings by applying artificial intelligence (AI) methods to a larger dataset to identify key features that predict fear reactivity (i.e., immediate reaction to fear inducing stimuli) and fearfulness (i.e., a stable personality trait) in 101 Lipizzan horses. The analysis included 221 morphological, kinematic, behavioral and management measurements per horse. Previous findings were confirmed, as body and head size were identified as promising predictors of aspects of fear-related trait. Using an iterative AI approach, six key features for fear reactivity and nine for...
Short-Term Impact of Dry Needling Treatment for Myofascial Pain on Equine Biomechanics Through Artificial Intelligence-Based Gait Analysis. Myofascial pain syndrome (MPS) is a common source of musculoskeletal pain, characterized by trigger points (TrPs). In horses, MPS is frequently underdiagnosed, and evidence on DN effectiveness is limited. This study investigated whether DN can improve the biomechanics in horses using an artificial intelligence (AI)-based markerless smartphone application (app). Fourteen horses participated, including nine used in assisted therapy, four leisure horses, and one with mixed use. The presence of TrPs was evaluated in six muscles through manual palpation: brachiocephalicus, trapezius, gluteus medius...
Correction: Automated recognition of emotional states of horses from facial expressions. [This corrects the article DOI: 10.1371/journal.pone.0302893.].
Convolutional Neural Networks Assisted Peak Classification in Targeted LC-HRMS/MS for Equine Doping Control Screening Analyses. Doping control screening analyses usually involve visual inspection of extracted ion chromatograms (EIC) by a trained analytical chemist, followed by further investigations if needed. This task is both highly repetitive and time-consuming, given the hundreds of compounds and metabolites to be screened in tens of thousands of samples per year. With the recent widespread adoption of machine learning in analytical chemistry and the training of high-performance convolutional neural networks (CNN), these operations can be automated with high accuracy and throughput. Applying this technology to dopi...
Validation of Vetscan Imagyst®, a diagnostic test utilizing an artificial intelligence deep learning algorithm, for detecting strongyles and Parascaris spp. in equine fecal samples. Current methods for obtaining fecal egg counts in horses are often inaccurate and variable depending on the analyst's skill and experience. Automated digital scanning of fecal sample slides integrated with analysis by an artificial intelligence (AI) algorithm is a viable, emerging alternative that can mitigate operator variation compared to conventional methods in companion animal fecal parasite diagnostics. Vetscan Imagyst is a novel fecal parasite detection system that uploads the scanned image to the cloud where proprietary software analyzes captured images for diagnostic recognition by a d...
Advancements in Subchondral Bone Biomechanics: Insights from Computed Tomography and Micro-Computed Tomography Imaging in Equine Models. This review synthesizes recent advancements in understanding subchondral bone (SCB) biomechanics using computed tomography (CT) and micro-computed tomography (micro-CT) imaging in large animal models, particularly horses. Results: Recent studies highlight the complexity of SCB biomechanics, revealing variability in density, microstructure, and biomechanical properties across the depth of SCB from the joint surface, as well as at different joint locations. Early SCB abnormalities have been identified as predictive markers for both osteoarthritis (OA) and stress fractures. The development of sta...
[Methods for parturition monitoring in the mare – an overview]. Various systems are available for birth monitoring in horses, whereby a distinction must be made between methods for more accurate prediction of the date of birth in order to intensify monitoring of the mare in a timely manner as well as methods for detecting individuals that are in labor. Basically, it should be noted that there are almost no studies that compare different methods on the same population of mares. As the time of birth approaches, physiological parameters of mare and fetus change, but their variability is too high to predict the exact parturition time point prospectively. The b...
From facial expressions to algorithms: a narrative review of animal pain recognition technologies. Facial expressions are essential for communication and emotional expression across species. Despite the improvements brought by tools like the Horse Grimace Scale (HGS) in pain recognition in horses, their reliance on human identification of characteristic traits presents drawbacks such as subjectivity, training requirements, costs, and potential bias. Despite these challenges, the development of facial expression pain scales for animals has been making strides. To address these limitations, Automated Pain Recognition (APR) powered by Artificial Intelligence (AI) offers a promising advancement...
Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases. The aim was to compare ophthalmic diagnoses made by veterinarians to a deep learning (artificial intelligence) software tool which was developed to aid in the diagnosis of equine ophthalmic diseases. As equine ophthalmology is a very specialised field in equine medicine, the tool may be able to help in diagnosing equine ophthalmic emergencies such as uveitis. Methods: In silico tool development and assessment of diagnostic performance. Methods: A deep learning tool which was developed and trained for classification of equine ophthalmic diseases was tested with 40 photographs displaying various...
Objective movement asymmetry in horses is comparable between markerless technology and sensor-based systems. A markerless artificial intelligence (AI) system for lameness detection has recently become available but has not been extensively compared with commonly used inertial measurement unit (IMU) systems for detecting asymmetry under field conditions. Objective: Comparison of classification of asymmetric limbs under field conditions and comparison of normalised asymmetry data using a markerless AI system (SleipAI; recorded on a tripod mounted iPhone 14pro [SL]); the Equinosis Q Lameness Locator (LL); the EquiMoves (EM); and subjective evaluation (SE). Methods: Descriptive clinical study. Methods: S...
Objective Assessment of Equine Locomotor Symmetry Using an Inertial Sensor System and Artificial Intelligence: A Comparative Study. In horses, quantitative assessment of gait parameters, as with the use of inertial measurement units (IMUs) systems, might help in the decision-making process. However, it requires financial investment, is time-consuming, and lacks accuracy if displaced. An innovative artificial intelligence marker-less motion tracking system (AI-MTS) may overcome these limitations in the field. Our aim was to compare the level of agreement and accuracy between both systems and visual clinical assessment. Twenty horses underwent locomotion analysis by visual assessment, IMUs, and AI-MTS systems, under the foll...
The future of equine semen analysis. We are currently experiencing a period of rapid advancement in various areas of science and technology. The integration of high throughput 'omics' techniques with advanced biostatistics, and the help of artificial intelligence, is significantly impacting our understanding of sperm biology. These advances will have an appreciable impact on the practice of reproductive medicine in horses. This article provides a brief overview of recent advances in the field of spermatology and how they are changing assessment of sperm quality. This article is written from the authors' perspective, using the sta...
Automatic early detection of induced colic in horses using accelerometer devices. To seek appropriate veterinary attention for horses with colic, owners must recognise early signs. Direct observation of horse behaviour has several drawbacks: it is time-consuming, hard to see subtle and common behavioural signs, and is based on intuition and subjective decisions. Due to recent advances in wearables and artificial intelligence, it may be possible to develop diagnostic software that can automatically detect colic signs. Objective: To develop a software algorithm to aid in the detection of colic signs and levels of pain. Methods: In vivo experiments. Methods: Transient colic wa...
Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses. With an increasing number of systems for quantifying lameness-related movement asymmetry, between-system comparisons under non-laboratory conditions are important for multi-centre or referral-level studies. This study compares an artificial intelligence video app to a validated inertial measurement unit (IMU) gait analysis system in a specific group of horses. Methods: Twenty-two reining Quarter horses were equipped with nine body-mounted IMUs while being videoed with a smartphone app. Both systems quantified head and pelvic movement symmetry during in-hand trot (hard/soft ground) and on the l...
Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review. The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English langu...
Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain. The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based ...
Classification of racehorse limb radiographs using deep convolutional neural networks. To assess the capability of deep convolutional neural networks to classify anatomical location and projection from a series of 48 standard views of racehorse limbs. Unassigned: Radiographs ( = 9504) of horse limbs from image sets made for veterinary inspections by 10 independent veterinary clinics were used to train, validate and test (116, 40 and 42 radiographs, respectively) six deep learning architectures available as part of the open source machine learning framework PyTorch. The deep learning architectures with the best top-1 accuracy had the batch size further investigated. Unassigned: T...
Is Markerless More or Less? Comparing a Smartphone Computer Vision Method for Equine Lameness Assessment to Multi-Camera Motion Capture. Computer vision is a subcategory of artificial intelligence focused on extraction of information from images and video. It provides a compelling new means for objective orthopaedic gait assessment in horses using accessible hardware, such as a smartphone, for markerless motion analysis. This study aimed to explore the lameness assessment capacity of a smartphone single camera (SC) markerless computer vision application by comparing measurements of the vertical motion of the head and pelvis to an optical motion capture multi-camera (MC) system using skin attached reflective markers. Twenty-five...
A Review of Radiomics and Artificial Intelligence and Their Application in Veterinary Diagnostic Imaging. Great advances have been made in human health care in the application of radiomics and artificial intelligence (AI) in a variety of areas, ranging from hospital management and virtual assistants to remote patient monitoring and medical diagnostics and imaging. To improve accuracy and reproducibility, there has been a recent move to integrate radiomics and AI as tools to assist clinical decision making and to incorporate it into routine clinical workflows and diagnosis. Although lagging behind human medicine, the use of radiomics and AI in veterinary diagnostic imaging is becoming more frequent...
Artificial Intelligence for Lameness Detection in Horses-A Preliminary Study. Lameness in horses is a long-known issue influencing the welfare, as well as the use, of a horse. Nevertheless, the detection and classification of lameness mainly occurs on a subjective basis by the owner and the veterinarian. The aim of this study was the development of a lameness detection system based on pose estimation, which permits non-invasive and easily applicable gait analysis. The use of 58 reference points on easily detectable anatomical landmarks offers various possibilities for gait evaluation using a simple setup. For this study, three groups of horses were used: one training gr...
Improving energy consumption prediction for residential buildings using Modified Wild Horse Optimization with Deep Learning model. The consumption of a significant quantity of energy in buildings has been linked to the emergence of environmental problems that can have unfavourable effects on people. The prediction of energy consumption is widely regarded as an effective method for the conservation of energy and the improvement of decision-making processes for the purpose of lowering energy use. When it comes to the generation of positive results in prediction tasks, the Machine Learning (ML) technique can be considered the most appropriate and applicable strategy. This article presents a Modified Wild Horse Optimization w...
Assessing the utility value of Hucul horses using classification models, based on artificial neural networks. The aim of this study was to evaluate factors influencing the performance of Hucul horses and to develop a prediction model, based on artificial neural (AI) networks for predict horses' classification, relying on their performance value assessment during the annual Hucul championships. The Feedforward multilayer artificial neural networks, learned using supervised methods and implemented in Matlab programming environment were applied. Artificial neural networks with one and two hidden layers with different numbers of neurons equipped with a tangensoidal transition function, learned using the L...
Binary Horse herd optimization algorithm with crossover operators for feature selection. This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are al...
Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis. Due to recent developments in artificial intelligence, deep learning, and smart-device-technology, diagnostic software may be developed which can be executed offline as an app on smartphones using their high-resolution cameras and increasing processing power to directly analyse photos taken on the device. Objective: A software tool was developed to aid in the diagnosis of equine ophthalmic diseases, especially uveitis. Methods: Prospective comparison of software and clinical diagnoses. Methods: A deep learning approach for image classification was used to train software by analysing photograph...
