Artificial Neural Networks (ANNs) are computational models inspired by the human brain, designed to recognize patterns and solve complex problems by learning from data. In equine research, ANNs can be applied to various domains, including predicting performance outcomes, diagnosing health conditions, and analyzing gait patterns in horses. These models process input data through interconnected nodes or "neurons," adjusting their connections based on the data received to improve accuracy over time. The use of ANNs in equine studies offers a data-driven approach to understanding and interpreting complex biological and behavioral phenomena. This page compiles peer-reviewed research studies and scholarly articles that explore the application, methodology, and potential of artificial neural networks in advancing equine science.
Manzoori S, Farahani AHK, Moradi MH, Kazemi-Bonchenari M.The assignment of an individual to the true population of origin using a low-panel of discriminant SNP markers is one of the most important applications of genomic data for practical use. The aim of this study was to evaluate the potential of different Artificial Neural Networks (ANNs) approaches consisting Deep Neural Networks (DNN), Garson and Olden methods for feature selection of informative SNP markers from high-throughput genotyping data, that would be able to trace the true breed of unknown samples. The total of 795 animals from 37 breeds, genotyped by using the Illumina SNP 50k Bead ch...
Lawin FJ, Byström A, Roepstorff C, Rhodin M, Almlöf M, Silva M, Andersen PH, Kjellström H, Hernlund E.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...
Parmentier JIM, Bosch S, van der Zwaag BJ, Weishaupt MA, Gmel AI, Havinga PJM, van Weeren PR, Braganca FMS.Vertical ground reaction force (GRFz) measurements are the best tool for assessing horses' weight-bearing lameness. However, collection of these data is often impractical for clinical use. This study evaluates GRFz predicted using data from body-mounted IMUs and long short-term memory recurrent neural networks (LSTM-RNN). Twenty-four clinically sound horses, equipped with IMUs on the upper-body (UB) and each limb, walked and trotted on a GRFz measuring treadmill (TiF). Both systems were time-synchronised. Data from randomly selected 16, 4, and 4 horses formed training, validation, and test dat...
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...
Mouloodi S, Rahmanpanah H, Burvill C, Davies HMS.The hierarchical nature of bone makes it a difficult material to fully comprehend. The equine third metacarpal (MC3) bone experiences nonuniform surface strains, which are a measure of displacement induced by loads. This paper investigates the use of an artificial neural network expert system to quantify MC3 bone loading. Previous studies focused on determining the response of bone using load, bone geometry, mechanical properties, and constraints as input parameters. This is referred to as a forward problem and is generally solved using numerical techniques such as finite element analysis (FEA...
Sarin JK, Te Moller NCR, Mancini IAD, Brommer H, Visser J, Malda J, van Weeren PR, Afara IO, Töyräs J.Arthroscopic assessment of articular tissues is highly subjective and poorly reproducible. To ensure optimal patient care, quantitative techniques (e.g., near infrared spectroscopy (NIRS)) could substantially enhance arthroscopic diagnosis of initial signs of post-traumatic osteoarthritis (PTOA). Here, we demonstrate, for the first time, the potential of arthroscopic NIRS to simultaneously monitor progressive degeneration of cartilage and subchondral bone in vivo in Shetland ponies undergoing different experimental cartilage repair procedures. Osteochondral tissues adjacent to the repair sites...
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...
Savelberg HH, Van Loon T, Schamhardt HC.An artificial neural network (ANN) was developed to investigate whether hoof wall deformation could be used to determine ground reaction forces (GRF) in horses. The ANN was taught this relationship under certain conditions and was able to generalise this knowledge to conditions for which it was not trained before. To acquire data to train and test the ANN, a horse was equipped with strain gauges attached to the dorsal, lateral and medial parts of the hoof to assess hoof wall deformation. A force plate was used to measure the GRFs. Both hoof wall deformation and GRF were recorded simultaneously...
Clayton HM.The success of a clinical gait laboratory depends on choosing the right equipment, installing it correctly, running calibration checks, and having skilled technical and professional personnel. For kinematic analysis, videographic or optoelectronic systems are the method of choice, with 2-D data being adequate for the majority of equine evaluations. A force plate provides a precise description of the 3-D ground reaction force; transmission of the force through the body tissues is measured using strain gauges attached to the bones and tendons. Accelerometers bonded to the hoof wall provide infor...
Sarin JK, Te Moller NCR, Mancini IAD, Brommer H, Visser J, Malda J, van Weeren PR, Afara IO, Töyräs J.Arthroscopic assessment of articular tissues is highly subjective and poorly reproducible. To ensure optimal patient care, quantitative techniques (e.g., near infrared spectroscopy (NIRS)) could substantially enhance arthroscopic diagnosis of initial signs of post-traumatic osteoarthritis (PTOA). Here, we demonstrate, for the first time, the potential of arthroscopic NIRS to simultaneously monitor progressive degeneration of cartilage and subchondral bone in vivo in Shetland ponies undergoing different experimental cartilage repair procedures. Osteochondral tissues adjacent to the repair sites...
Mouloodi S, Rahmanpanah H, Burvill C, Davies HMS.The hierarchical nature of bone makes it a difficult material to fully comprehend. The equine third metacarpal (MC3) bone experiences nonuniform surface strains, which are a measure of displacement induced by loads. This paper investigates the use of an artificial neural network expert system to quantify MC3 bone loading. Previous studies focused on determining the response of bone using load, bone geometry, mechanical properties, and constraints as input parameters. This is referred to as a forward problem and is generally solved using numerical techniques such as finite element analysis (FEA...
Manzoori S, Farahani AHK, Moradi MH, Kazemi-Bonchenari M.The assignment of an individual to the true population of origin using a low-panel of discriminant SNP markers is one of the most important applications of genomic data for practical use. The aim of this study was to evaluate the potential of different Artificial Neural Networks (ANNs) approaches consisting Deep Neural Networks (DNN), Garson and Olden methods for feature selection of informative SNP markers from high-throughput genotyping data, that would be able to trace the true breed of unknown samples. The total of 795 animals from 37 breeds, genotyped by using the Illumina SNP 50k Bead ch...
Clayton HM.The success of a clinical gait laboratory depends on choosing the right equipment, installing it correctly, running calibration checks, and having skilled technical and professional personnel. For kinematic analysis, videographic or optoelectronic systems are the method of choice, with 2-D data being adequate for the majority of equine evaluations. A force plate provides a precise description of the 3-D ground reaction force; transmission of the force through the body tissues is measured using strain gauges attached to the bones and tendons. Accelerometers bonded to the hoof wall provide infor...
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...
Lawin FJ, Byström A, Roepstorff C, Rhodin M, Almlöf M, Silva M, Andersen PH, Kjellström H, Hernlund E.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...
Parmentier JIM, Bosch S, van der Zwaag BJ, Weishaupt MA, Gmel AI, Havinga PJM, van Weeren PR, Braganca FMS.Vertical ground reaction force (GRFz) measurements are the best tool for assessing horses' weight-bearing lameness. However, collection of these data is often impractical for clinical use. This study evaluates GRFz predicted using data from body-mounted IMUs and long short-term memory recurrent neural networks (LSTM-RNN). Twenty-four clinically sound horses, equipped with IMUs on the upper-body (UB) and each limb, walked and trotted on a GRFz measuring treadmill (TiF). Both systems were time-synchronised. Data from randomly selected 16, 4, and 4 horses formed training, validation, and test dat...
Savelberg HH, Van Loon T, Schamhardt HC.An artificial neural network (ANN) was developed to investigate whether hoof wall deformation could be used to determine ground reaction forces (GRF) in horses. The ANN was taught this relationship under certain conditions and was able to generalise this knowledge to conditions for which it was not trained before. To acquire data to train and test the ANN, a horse was equipped with strain gauges attached to the dorsal, lateral and medial parts of the hoof to assess hoof wall deformation. A force plate was used to measure the GRFs. Both hoof wall deformation and GRF were recorded simultaneously...
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...
