Machine learning is a branch of artificial intelligence that involves the development of algorithms and statistical models enabling computers to perform tasks without explicit instructions. In the context of equine research, machine learning can be applied to analyze large datasets related to horse health, behavior, and performance. It can assist in identifying patterns and correlations that may not be immediately apparent through traditional analysis methods. Applications of machine learning in equine studies include predicting disease outbreaks, assessing gait abnormalities, and optimizing breeding strategies. This page compiles peer-reviewed research studies and scholarly articles that explore the applications, methodologies, and outcomes of using machine learning techniques in the study of equine science.
Alves NC, Freitas MM, Faria JRD, Horta CL, Martins-Filho OA, Araújo MSS, Costa GMJ, Costa EA, de Almeida FRL, Amaral PHR, Pérez JCG, Lana ÂQ....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, ...
Gomes C, Coheur L, Tilley P.Equine asthma is a prevalent respiratory disease that negatively impacts horses' health and athletic performance. Traditional diagnostic methods are invasive and require specialised equipment. There is a need for a non-invasive, cost-effective screening tool that can be used by veterinarians and horse handlers in ambulatory settings. Objective: To assess the willingness of veterinarians and horse handlers to adopt such a tool (Questionnaire 1) and the challenges associated with visually recognising equine asthma (Questionnaire 2) and to develop EquiBreathe, an artificial intelligence (AI)-powe...
Chodkiewicz A, Prończuk M, Studnicki M.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...
Lai CS, Wong ASY, Wong KS, Wan TSM, Ho ENM.In the screening of prohibited substances (PS) in horse biological samples with gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) for doping control, an enormous number of chromatograms are generated. Reviewing these chromatograms to identify suspicious findings requires an extensive manual effort. Recent advancements in Artificial Intelligence (AI) enable its use to classify images into different categories. This can potentially be utilized to perform first-line analysis of chromatograms, which are usually displayed as images, by classifying them...
Macleod BM, Wilkins PA, McCoy AM, Bishop RC.Viscoelastic coagulation testing (VCT) identifies subclinical disruption of coagulation homeostasis and may improve prognostication, particularly for patients with severe systemic inflammation or shock. Machine learning (ML) algorithms may capture complex relationships between clinical variables better than linear regression (GLM). Objective: To evaluate the utility of ML models incorporating VCT and clinical data to predict survival outcomes in horses with acute abdominal pain. Methods: Retrospective observational cohort study. Methods: VCT (VCM Vet™) was performed on 57 horses with acute a...
Cheung HW, Wong KS, Cheng PCF, Tsang CYN, Farrington AF, Wan TSM, Ho ENM.Erythropoiesis-stimulating agents (ESAs) continue to be a significant threat to the integrity of human and equine sports. Besides conventional direct testing, monitoring the biomarkers associated with the effects of ESAs may provide a complementary approach via indirect detection to enhance doping control. In this study, we applied RNA-sequencing (RNA-seq) to discover blood RNA biomarkers in Thoroughbred horses after administration with a long-acting form of recombinant human erythropoietin (rhEPO), methoxy polyethylene glycol epoetin beta, Mircera®. A single subcutaneous administration of Mi...
Cetintav B, Yalcin A.Understanding equine behavior through advanced monitoring technologies is crucial for improving animal welfare, optimizing training strategies, and enabling early detection of health or stress-related issues. This study integrates wearable sensor data with Explainable Artificial Intelligence (XAI) techniques, particularly SHAP (Shapley Additive Explanations), to enhance interpretability in equine behavior classification. The data used in this study were sourced from an open-source dataset, ensuring transparency and reproducibility. Orginally, data were collected from 18 horses using sensor dev...
Turek B, Pawlikowski M, Jankowski K, Borowska M, Skierbiszewska K, Jasiński T, Domino M.In veterinary medicine, conventional radiography is the first-choice method for most diagnostic imaging applications in both small animal and equine practice. One direction in its development is the integration of bone density evaluation and artificial intelligence-assisted clinical decision-making, which is expected to enhance and streamline veterinarians' daily practices. One such decision-support method is k-means clustering, a machine learning and data mining technique that can be used clinically to classify radiographic signs into healthy or affected clusters. The study aims to investigat...
Feighelstein M, Ricci-Bonot C, Hasan H, Weinberg H, Rettig T, Segal M, Distelfeld T, Shimshoni I, Mills DS, Zamansky A.[This corrects the article DOI: 10.1371/journal.pone.0302893.].
Poizat E, Gérard M, Macaire C, De Azevedo E, Denoix JM, Coudry V, Jacquet S, Bertoni L, Tallaj A, Audigié F, Hatrisse C, Hébert C, Martin P....Lameness detection in horses is a critical challenge in equine veterinary practice, particularly when symptoms are mild. This study aimed to develop a predictive system using a support vector machine (SVM) to identify the affected limb in horses trotting in a straight line. The system analyzed data from inertial measurement units (IMUs) placed on the horse's head, withers, and pelvis, using variables such as vertical displacement and retraction angles. A total of 287 horses were included, with 256 showing single-limb lameness and 31 classified as sound. The model achieved an overall accuracy o...
Bhave A, Kieson E, Hafner A, Gloor PA.This research applies unsupervised learning on a large original dataset of horses in the wild to identify previously unidentified horse emotions. We construct a novel, high-quality, diverse dataset of 3929 images consisting of five wild horse breeds worldwide at different geographical locations. We base our analysis on the seven Panksepp emotions of mammals "Exploring", "Sadness", "Playing", "Rage", "Fear", "Affectionate" and "Lust", along with one additional emotion "Pain" which has been shown to be highly relevant for horses. We apply the contrastive learning framework MoCo (Momentum Contras...
Bogossian PM, Nattala U, Wong ASM, Morrice-West AV, Zhang GZ, Rana P, Whitton RC, Hitchens PL.Decreasing speed and stride length over successive races have been shown to be associated with musculoskeletal injury (MSI) in racehorses, demonstrating the potential for early detection of MSI through longitudinal monitoring of changes in stride characteristics. A machine learning (ML) approach for early detection of MSI, enforced rest, and retirement events using this same horse-level, race-level, and stride characteristic data across all race sectionals was investigated. A CatBoost model using features from the two races prior to an event had the highest classification performance (sensitiv...
Elbourne M, Keledjian J, Cawley A, Fu S.Altrenogest, also known as allyltrenbolone, is a synthetic form of progesterone used therapeutically to suppress unwanted symptoms of estrus in female horses. Altrenogest affects the system by decreasing levels of endogenous gonadotrophin and luteinizing and follicle-stimulating hormones, which in turn decreases estrogen and mimics the increase of progesterone production. This results in more manageable mares for training and competition alongside male horses while improving the workplace safety of riders and handlers. However, when altrenogest is administered, prohibited steroid impurities su...
Bussiman F, Alves AAC, Richter J, Hidalgo J, Veroneze R, Oliveira T.Gait scores are widely used in the genetic evaluation of horses. However, the nature of such measurement may limit genetic progress since there is subjectivity in phenotypic information. This study aimed to assess the application of machine learning techniques in the prediction of breeding values for five visual gait scores in Campolina horses: dissociation, comfort, style, regularity, and development. The dataset contained over 5000 phenotypic records with 107,951 horses (14 generations) in the pedigree. A fixed model was used to estimate least-square solutions for fixed effects and adjusted ...
Tahir AM, Guo L, Ward RK, Yu X, Rideout A, Hore M, Wang ZJ.Laryngeal hemiplegia (LH) is a major upper respiratory tract (URT) complication in racehorses. Endoscopy imaging of horse throat is a gold standard for URT assessment. However, current manual assessment faces several challenges, stemming from the poor quality of endoscopy videos and subjectivity of manual grading. To overcome such limitations, we propose an explainable machine learning (ML)-based solution for efficient URT assessment. Specifically, a cascaded YOLOv8 architecture is utilized to segment the key semantic regions and landmarks per frame. Several spatiotemporal features are then ex...
Feighelstein M, Riccie-Bonot C, Hasan H, Weinberg H, Rettig T, Segal M, Distelfeld T, Shimshoni I, Mills DS, Zamansky A.Animal affective computing is an emerging new field, which has so far mainly focused on pain, while other emotional states remain uncharted territories, especially in horses. This study is the first to develop AI models to automatically recognize horse emotional states from facial expressions using data collected in a controlled experiment. We explore two types of pipelines: a deep learning one which takes as input video footage, and a machine learning one which takes as input EquiFACS annotations. The former outperforms the latter, with 76% accuracy in separating between four emotional states...
Nissen SD, Saljic A, Carstensen H, Braunstein TH, Hesselkilde EM, Kjeldsen ST, Hopster-Iversen C, D'Souza A, Jespersen T, Buhl R.Second-degree atrioventricular (AV) block at rest is very common in horses. The underlying molecular mechanisms are unexplored, but commonly attributed to high vagal tone. Unassigned: To assess whether AV block in horses is due to altered expression of the effectors of vagal signalling in the AV node, with specific emphasis on the muscarinic acetylcholine receptor (M) and the G protein-gated inwardly rectifying K (GIRK4) channel that mediates the cardiac current. Unassigned: Eighteen horses with a low burden of second-degree AV block (median 8 block per 20 h, IQR: 32 per 20 h) were assign...
Krishnan BS, Jones LR, Elmore JA, Samiappan S, Evans KO, Pfeiffer MB, Blackwell BF, Iglay RB.Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We cla...
Aguilar-Lazcano CA, Espinosa-Curiel IE, Ríos-Martínez JA, Madera-Ramírez FA, Pérez-Espinosa H.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...
Aqeel I, Khormi IM, Khan SB, Shuaib M, Almusharraf A, Alam S, Alkhaldi NA.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 ...
Darbandi H, Munsters C, Parmentier J, Havinga P.Detection of fatigue helps prevent injuries and optimize the performance of horses. Previous studies tried to determine fatigue using physiological parameters. However, measuring the physiological parameters, e.g., plasma lactate, is invasive and can be affected by different factors. In addition, the measurement cannot be done automatically and requires a veterinarian for sample collection. This study investigated the possibility of detecting fatigue non-invasively using a minimum number of body-mounted inertial sensors. Using the inertial sensors, sixty sport horses were measured during walk ...
Donnelly CG, Johnson AL, Reed S, Finno CJ.Cervical vertebral compressive myelopathy (CVCM) and equine neuroaxonal dystrophy/degenerative myeloencephalopathy (eNAD/EDM) are leading causes of spinal ataxia in horses. The conditions can be difficult to differentiate, and there is currently no diagnostic modality that offers a definitive antemortem diagnosis. Objective: Evaluate novel proteomic techniques and machine learning algorithms to predict biomarkers that can aid in the antemortem diagnosis of noninfectious spinal ataxia in horses. Methods: Banked serum and cerebrospinal fluid (CSF) samples from necropsy-confirmed adult eNAD/EDM (...
Costa da Silva RG, Mishra AP, Riggs CM, Doube M.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...
Davíðsson HB, Rees T, Ólafsdóttir MR, Einarsson H.Automated gait classification has traditionally been studied using horse-mounted sensors. However, smartphone-based sensors are more accessible, but the performance of gait classification models using data from such sensors has not been widely known or accessible. In this study, we performed horse gait classification using deep learning models and data from mobile phone sensors located in the rider's pocket. We gathered data from 17 horses and 14 riders. The data were gathered simultaneously from movement sensors in a mobile phone located in the rider's pocket and a gait classification system ...
Vasanthkumar P, Senthilkumar N, Rao KS, Metwally ASM, Fattah IM, Shaafi T, Murugan VS.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...
Mirmojarabian SA, Kajabi AW, Ketola JHJ, Nykänen O, Liimatainen T, Nieminen MT, Nissi MJ, Casula V.Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage. To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content. Retrospective, animal model. An open-source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points...
Satoła A, Łuszczyński J, Petrych W, Satoła K.Knowledge of the body weight of horses permits breeders to provide appropriate feeding and care regimen and allows veterinarians to monitor the animals' health. It is not always possible to perform an accurate measurement of the body weight of horses using horse weighbridges, and therefore, new body weight formulas based on biometric measurements are required. The objective of this study is to develop and validate models for estimating body weight in Icelandic foals using machine learning methods. The study was conducted using 312 data records of body measurements on 24 Icelandic foals (12 col...
ELKhamary AN, Keenihan EK, Schnabel LV, Redding WR, Schumacher J.While MRI is the modality of choice for the diagnosis of longitudinal tears (LTs) of the deep digital flexor tendon (DDFT) of horses, differentiating between various grades of tears based on imaging characteristics is challenging due to overlapping imaging features. In this retrospective, exploratory, diagnostic accuracy study, a machine learning (ML) scheme was applied to link quantitative features and qualitative descriptors to leverage MRI characteristics of different grades of tearing of the DDFT of horses. A qualitative MRI characteristic scheme, combining tendon morphologic features, alt...
Huang YH, Lyle JV, Ab Razak AS, Nandi M, Marr CM, Huang CL, Aston PJ, Jeevaratnam K.Atrial fibrillation (AF) is a common cardiac arrhythmia in both human and equine populations. It is associated with adverse outcomes in humans and decreased athletic performance in both populations. Paroxysmal atrial fibrillation (PAF) presents with intermittent, self-terminating AF episodes, and is difficult to diagnose once sinus rhythm resumes. Unassigned: We aimed to detect PAF subjects from normal sinus rhythm equine electrocardiograms (ECGs) using the Symmetric Projection Attractor Reconstruction (SPAR) method to encapsulate the waveform morphology and variability as the basis of a machi...
Awadallah MA, Hammouri AI, Al-Betar MA, Braik MS, Elaziz MA.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...
Andersen PH, Broomé S, Rashid M, Lundblad J, Ask K, Li Z, Hernlund E, Rhodin M, Kjellström H.Automated recognition of human facial expressions of pain and emotions is to a certain degree a solved problem, using approaches based on computer vision and machine learning. However, the application of such methods to horses has proven difficult. Major barriers are the lack of sufficiently large, annotated databases for horses and difficulties in obtaining correct classifications of pain because horses are non-verbal. This review describes our work to overcome these barriers, using two different approaches. One involves the use of a manual, but relatively objective, classification system for...
Lencioni GC, de Sousa RV, de Souza Sardinha EJ, Corrêa RR, Zanella AJ.The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral ch...
Serra Bragança FM, Broomé S, Rhodin M, Björnsdóttir S, Gunnarsson V, Voskamp JP, Persson-Sjodin E, Back W, Lindgren G, Novoa-Bravo M, Gmel AI....For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several ...
Marzahl C, Aubreville M, Bertram CA, Stayt J, Jasensky AK, Bartenschlager F, Fragoso-Garcia M, Barton AK, Elsemann S, Jabari S, Krauth J, Madhu P....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...
Mitchell KJ, Schwarzwald CC.Heart rate variability (HRV) analysis has been performed on ECG-derived data sets for more than 170 years but is currently undergoing a rapid evolution, thanks to the expansion of the human and veterinary medical technology sector. Traditional HRV analysis was initially performed to identify changes in vago-sympathetic balance, while the most recent focus has expanded to include the use of complex computer algorithms, neural networks and machine learning technology to identify cardiac arrhythmias, particularly atrial fibrillation (AF). Some of these techniques have recently been translated for...
Fraiwan MA, Abutarbush SM.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...
Mouloodi S, Rahmanpanah H, Gohari S, Burvill C, Tse KM, Davies HMS.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...
Aguilar-Lazcano CA, Espinosa-Curiel IE, Ríos-Martínez JA, Madera-Ramírez FA, Pérez-Espinosa H.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...
Sarin JK, Te Moller NCR, Mohammadi A, Prakash M, Torniainen J, Brommer H, Nippolainen E, Shaikh R, Mäkelä JTA, Korhonen RK, van Weeren PR, Afara IO....To assess the potential of near-infrared spectroscopy (NIRS) for in vivo arthroscopic monitoring of cartilage defects. Sharp and blunt cartilage grooves were induced in the radiocarpal and intercarpal joints of Shetland ponies and monitored at baseline (0 weeks) and at three follow-up timepoints (11, 23, and 39 weeks) by measuring near-infrared spectra in vivo at and around the grooves. The animals were sacrificed after 39 weeks and the joints were harvested. Spectra were reacquired ex vivo to ensure reliability of in vivo measurements and for reference analyses. Additionally, cartilage thickn...
Cain JL, Slusarewicz P, Rutledge MH, McVey MR, Wielgus KM, Zynda HM, Wehling LM, Scare JA, Steuer AE, Nielsen MK.Fecal egg counts are the cornerstone of equine parasite control programs. Previous work led to the development of an automated, image-analysis-based parasite egg counting system. The system has been further developed to include an automated reagent dispenser unit and a custom camera (CC) unit that generates higher resolution images, as well as a particle shape analysis (PSA) algorithm and machine learning (ML) algorithm. The first aim of this study was to conduct a comprehensive comparison of method precision between the original smartphone (SP) unit with the PSA algorithm, CC/PSA, CC/ML, and ...
Darbandi H, Serra Bragança F, van der Zwaag BJ, Voskamp J, Gmel AI, Haraldsdóttir EH, Havinga P.Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between bree...
Krishnan BS, Jones LR, Elmore JA, Samiappan S, Evans KO, Pfeiffer MB, Blackwell BF, Iglay RB.Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We cla...
Mao A, Huang E, Gan H, Parkes RSV, Xu W, Liu K.With the recent advances in deep learning, wearable sensors have increasingly been used in automated animal activity recognition. However, there are two major challenges in improving recognition performance-multi-modal feature fusion and imbalanced data modeling. In this study, to improve classification performance for equine activities while tackling these two challenges, we developed a cross-modality interaction network (CMI-Net) involving a dual convolution neural network architecture and a cross-modality interaction module (CMIM). The CMIM adaptively recalibrated the temporal- and axis-wis...
Zhou M, Elmore JA, Samiappan S, Evans KO, Pfeiffer MB, Blackwell BF, Iglay RB.In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep l...
Putnová L, Štohl R.Considering the extensive data sets and statistical techniques, animal breeding embodies a branch of machine learning that has a constantly increasing impact on breeding. In our study, information regarding the potential of machine learning and data mining within a large set of horses and breeds is presented. The individual assignment methods and factors influencing the success rate of the procedure are compared at the Czech population scale. The fixation index values ranged from 0.057 (HMS1) to 0.144 (HTG6), and the overall genetic differentiation amounted to 8.9% among the breeds. The highes...
Schobesberger H, Peham C.The purpose of this study was to investigate whether artificial neural networks could be used to determine equine lameness by computational means only. The integral parts of our approach were the combination of automated signal tracking of horses on a treadmill and the computational power of artificial neural networks (ANN). The motion of 175 horses trotting on a treadmill was recorded using the SELSPOT II system for motion analysis. Two cameras traced infrared (IR) markers on the head and on the left forehoof. The motion of the head was Fourier-transformed and further processed by a multilaye...
Awadallah MA, Hammouri AI, Al-Betar MA, Braik MS, Elaziz MA.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...
Aqeel I, Khormi IM, Khan SB, Shuaib M, Almusharraf A, Alam S, Alkhaldi NA.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 ...
Schmutz A, Chèze L, Jacques J, Martin P.With the emergence of numerical sensors in sports, there is an increasing need for tools and methods to compute objective motion parameters with great accuracy. In particular, inertial measurement units are increasingly used in the clinical domain or the sports one to estimate spatiotemporal parameters. The purpose of the present study was to develop a model that can be included in a smart device in order to estimate the horse speed per stride from accelerometric and gyroscopic data without the use of a global positioning system, enabling the use of such a tool in both indoor and outdoor condi...
Kil N, Ertelt K, Auer U.Changes in behaviour are often caused by painful conditions. Therefore, the assessment of behaviour is important for the recognition of pain, but also for the assessment of quality of life. Automated detection of movement and the behaviour of a horse in the box stall should represent a significant advancement. In this study, videos of horses in an animal hospital were recorded using an action camera and a time-lapse mode. These videos were processed using the convolutional neural network Loopy for automated prediction of body parts. Development of the model was carried out in several steps, in...
Feighelstein M, Riccie-Bonot C, Hasan H, Weinberg H, Rettig T, Segal M, Distelfeld T, Shimshoni I, Mills DS, Zamansky A.Animal affective computing is an emerging new field, which has so far mainly focused on pain, while other emotional states remain uncharted territories, especially in horses. This study is the first to develop AI models to automatically recognize horse emotional states from facial expressions using data collected in a controlled experiment. We explore two types of pipelines: a deep learning one which takes as input video footage, and a machine learning one which takes as input EquiFACS annotations. The former outperforms the latter, with 76% accuracy in separating between four emotional states...
Lee JN, Lee MW, Byeon YH, Lee WS, Kwak KC.In this study, we classify four horse gaits (walk, sitting trot, rising trot, canter) of three breeds of horse (Jeju, Warmblood, and Thoroughbred) using a neuro-fuzzy classifier (NFC) of the Takagi-Sugeno-Kang (TSK) type from data information transformed by a wavelet packet (WP). The design of the NFC is accomplished by using a fuzzy c-means (FCM) clustering algorithm that can solve the problem of dimensionality increase due to the flexible scatter partitioning. For this purpose, we use the rider's hip motion from the sensor information collected by inertial sensors as feature data for the cla...
Darbandi H, Munsters C, Parmentier J, Havinga P.Detection of fatigue helps prevent injuries and optimize the performance of horses. Previous studies tried to determine fatigue using physiological parameters. However, measuring the physiological parameters, e.g., plasma lactate, is invasive and can be affected by different factors. In addition, the measurement cannot be done automatically and requires a veterinarian for sample collection. This study investigated the possibility of detecting fatigue non-invasively using a minimum number of body-mounted inertial sensors. Using the inertial sensors, sixty sport horses were measured during walk ...
Huang YH, Lyle JV, Ab Razak AS, Nandi M, Marr CM, Huang CL, Aston PJ, Jeevaratnam K.Atrial fibrillation (AF) is a common cardiac arrhythmia in both human and equine populations. It is associated with adverse outcomes in humans and decreased athletic performance in both populations. Paroxysmal atrial fibrillation (PAF) presents with intermittent, self-terminating AF episodes, and is difficult to diagnose once sinus rhythm resumes. Unassigned: We aimed to detect PAF subjects from normal sinus rhythm equine electrocardiograms (ECGs) using the Symmetric Projection Attractor Reconstruction (SPAR) method to encapsulate the waveform morphology and variability as the basis of a machi...
Burocziova M, Riha J.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...
Mirmojarabian SA, Kajabi AW, Ketola JHJ, Nykänen O, Liimatainen T, Nieminen MT, Nissi MJ, Casula V.Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage. To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content. Retrospective, animal model. An open-source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points...
Klingberg J, Cawley A, Shimmon R, Fu S.The constant evolution of the illicit drug market makes the identification of unknown compounds problematic. Obtaining certified reference materials for a broad array of new analogues can be difficult and cost prohibitive. Machine learning provides a promising avenue to putatively identify a compound before confirmation against a standard. In this study, machine learning approaches were used to develop class prediction and retention time prediction models. The developed class prediction model used a naïve Bayes architecture to classify opioids as belonging to either the fentanyl analogues, AH...
Satoła A, Łuszczyński J, Petrych W, Satoła K.Knowledge of the body weight of horses permits breeders to provide appropriate feeding and care regimen and allows veterinarians to monitor the animals' health. It is not always possible to perform an accurate measurement of the body weight of horses using horse weighbridges, and therefore, new body weight formulas based on biometric measurements are required. The objective of this study is to develop and validate models for estimating body weight in Icelandic foals using machine learning methods. The study was conducted using 312 data records of body measurements on 24 Icelandic foals (12 col...
Ganeshpurkar A, Singh R, Kumar D, Gutti G, Sardana D, Shivhare S, Singh RB, Kumar A, Singh SK.Machine learning (ML), an emerging field in drug design, has the potential to predict toxicity, shape-based analysis of inhibitors, scoring function (SF) etc. In the present study, a homology model, docking protocol, and a dedicated SF have been developed to identify the inhibitors of horse butyrylcholinesterase (BChE) enzyme. Horse BChE enzyme has homology with human BChE and is a substitute for the screening of inhibitors. The developed homology model was validated and the active site residues were identified from Cavityplus to generate grid box for docking. The validation of docking invol...
Dahl CD, Wyss C, Zuberbühler K, Bachmann I.One model of signal evolution is based on the notion that behaviours become increasingly detached from their original biological functions to obtain a communicative value. Selection may not always favour the evolution of such transitions, for instance, if signalling is costly due to predators usurping signal production. Here, we collected inertial movement sensing data recorded from multiple locations in free-ranging horses (Equus caballus), which we subjected to a machine learning algorithm to extract kinematic gestalt profiles. This yielded surprisingly rich and multi-layered sets of informa...
