Neural networks in the context of equine research refer to computational models inspired by the human brain's neural architecture, designed to recognize patterns and make predictions based on data. These models are increasingly applied in various aspects of equine science, including gait analysis, health monitoring, and behavior prediction. By processing large datasets, neural networks can identify subtle patterns and correlations that might be challenging to discern through traditional methods. This page gathers peer-reviewed research studies and scholarly articles that explore the applications, methodologies, and outcomes of utilizing neural networks in equine studies, highlighting their potential to enhance understanding and management of horse health and performance.
Scharre A, Scholler D, Gesell-May S, Müller T, Zablotski Y, Ertel W, May A.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...
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...
Koh RB, Harrison TM.Over the past years, the concept of pain management in veterinary medicine has evolved and led to the establishment of a new concept of multimodal approach to pain management, as the current standard of care. The use of multimodal analgesia combining pharmacologic and nonpharmacologic techniques not only helps optimize the quality and efficacy of analgesia but also may prevent the development of chronic or persistent pain. During the past decade, acupuncture has become more popular and evolved into one of the most used forms of integrative medicine interventions and nonpharmacologic therapeuti...
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...
Topczewska J, Bartman J, Kwater T.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...
May A, Gesell-May S, Müller T, Ertel W.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...
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...
Eerdekens A, Deruyck M, Fontaine J, Damiaans B, Martens L, De Poorter E, Govaere J, Plets D, Joseph W.Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models base...
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...
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...
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...
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...
Stover KK, Williams SH.In mammals, chewing cycle duration (CCD) increases with various measures of size, scaling with body mass(0.13-0.28) and jaw length(0.55). Proposed explanations for these scaling relationships include the allometry of body size, basal metabolic rate and tooth size, on the one hand, and pendular mechanics treating the jaw as a gravity-driven pendulum, on the other. Little is known, however, about the relationship between CCD and size within species. Recent research in dogs demonstrates altogether different scaling exponents and weaker correlations. This research suggests that breed-specific grow...
Keegan KG, Arafat S, Skubic M, Wilson DA, Kramer J.To investigate continuous wavelet transformation and neural network classification of gait data for detecting forelimb lameness in horses. Methods: 12 adult horses with mild forelimb lameness. Methods: Position of the head and right forelimb foot, metacarpophalangeal (ie, fetlock), carpal, and elbow joints was determined by use of kinematic analysis before and after palmar digital nerve blocks. We obtained 8 recordings from horses without lameness, 8 with right forelimb lameness, and 8 with left forelimb lameness. Vertical and horizontal position of the head and vertical position of the foot, ...
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...
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...
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...
May A, Gesell-May S, Müller T, Ertel W.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...
Stover KK, Williams SH.In mammals, chewing cycle duration (CCD) increases with various measures of size, scaling with body mass(0.13-0.28) and jaw length(0.55). Proposed explanations for these scaling relationships include the allometry of body size, basal metabolic rate and tooth size, on the one hand, and pendular mechanics treating the jaw as a gravity-driven pendulum, on the other. Little is known, however, about the relationship between CCD and size within species. Recent research in dogs demonstrates altogether different scaling exponents and weaker correlations. This research suggests that breed-specific grow...
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...
Eerdekens A, Deruyck M, Fontaine J, Damiaans B, Martens L, De Poorter E, Govaere J, Plets D, Joseph W.Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models base...
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...
Keegan KG, Arafat S, Skubic M, Wilson DA, Kramer J.To investigate continuous wavelet transformation and neural network classification of gait data for detecting forelimb lameness in horses. Methods: 12 adult horses with mild forelimb lameness. Methods: Position of the head and right forelimb foot, metacarpophalangeal (ie, fetlock), carpal, and elbow joints was determined by use of kinematic analysis before and after palmar digital nerve blocks. We obtained 8 recordings from horses without lameness, 8 with right forelimb lameness, and 8 with left forelimb lameness. Vertical and horizontal position of the head and vertical position of the foot, ...
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...
Koh RB, Harrison TM.Over the past years, the concept of pain management in veterinary medicine has evolved and led to the establishment of a new concept of multimodal approach to pain management, as the current standard of care. The use of multimodal analgesia combining pharmacologic and nonpharmacologic techniques not only helps optimize the quality and efficacy of analgesia but also may prevent the development of chronic or persistent pain. During the past decade, acupuncture has become more popular and evolved into one of the most used forms of integrative medicine interventions and nonpharmacologic therapeuti...
Topczewska J, Bartman J, Kwater T.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...
Scharre A, Scholler D, Gesell-May S, Müller T, Zablotski Y, Ertel W, May A.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...
