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.
What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research. Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary scientific domains where machines are provided with an approximation of human intelligence. The conjecture is that machines are able to learn from existing examples, and employ this accumulated knowledge to fulfil challenging tasks such as regression analysis, pattern classification, and prediction. The horse biomechanical models have been identified as an alternative tool to investigate the effects of mechanical loading and induced deformations on the tissues and structures in humans. Many reported invest...
Regulation of AI in Health Care: A Cautionary Tale Considering Horses and Zebras. The introduction of Artificial Intelligence (AI) into health care has been accompanied by uncertainties and regulatory challenges. The establishment of a regulatory framework around AI in health is in its infancy and the way forward is unclear. There are those who argue that this represents a concerning regulatory gap, while others assert that existing regulatory frameworks, policies and guidelines are sufficient. We argue that perhaps the reality is somewhere in between, but that there is a need for engagement with principles and guidelines to inform future regulation. However, this cannot be...
Comparative studies on faecal egg counting techniques used for the detection of gastrointestinal parasites of equines: A systematic review. Faecal egg counting techniques (FECT) form the cornerstone for the detection of gastrointestinal parasites in equines. For this purpose, several flotation, centrifugation, image- and artificial intelligence-based techniques are used, with varying levels of performance. This review aimed to critically appraise the literature on the assessment and comparison of various coprological techniques and/or modifications of these techniques used for equines and to identify the knowledge gaps and future research directions. We searched three databases for published scientific studies on the assessment an...
Novel Algorithms for Comprehensive Untargeted Detection of Doping Agents in Biological Samples. To address the limitations of current targeted analytical methods that can only detect known doping agents, a novel methodology that permits untargeted drug detection (UDD) has been developed to help in the fight against doping in sports. Fifty-seven drugs were spiked into blank equine plasma and were treated as unknowns since their exact masses and chromatographic retention times were not utilized for detection. The spiked drugs were extracted from the plasma samples and were analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The acquired LC-HRMS raw ...
Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides. Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated...
Using Artificial Intelligence to Predict Survivability Likelihood and Need for Surgery in Horses Presented With Acute Abdomen (Colic). Artificial intelligence and machine learning have promising applications in several medical fields of diagnosis, imaging, and laboratory testing procedures. However, the use of this technology in the veterinary medicine field is lagging behind, and there are many areas where it could be used with potentially successful outcomes and results. In this study, two critical predictions were explored in horses presented with acute abdomen (colic) using this technology. Those were the need for surgical intervention and survivability likelihood of affected horses based on clinical data (history, clinic...
Horse breed discrimination using machine learning methods. Genetic relationships and population structure of 8 horse breeds in the Czech and Slovak Republics were investigated using classification methods for breed discrimination. To demonstrate genetic differences among these breeds, we used genetic information - genotype data of microsatellite markers and classification algorithms - to perform a probabilistic prediction of an individual's breed. In total, 932 unrelated animals were genotyped for 17 microsatellite markers recommended by the ISAG for parentage testing (AHT4, AHT5, ASB2, HMS3, HMS6, HMS7, HTG4, HTG10, VHL20, HTG6, HMS2, HTG7, ASB17, AS...
