Artificial Intelligence for Lameness Detection in Horses-A Preliminary Study

Simple Summary In the expanding field of artificial intelligence, deep learning and smart-device-technology, a diagnostic software tool was developed, which can help distinguish between lame and sound horses and locate the affected limb. As lameness influences the welfare of horses and is often difficult to detect, this tool can help owners and veterinarians in the process of evaluation. The technology is based on pose estimation, which is already used in human and veterinary science to study movement of limbs or bodies without the need to fix any devices onto the object of interest. In this study, 22 horses with unilateral fore- or hindlimb lameness and a control group of eight sound horses were analysed with the program. Based on the results of the program, it was possible to differentiate between horses with fore- and hindlimb lameness and sound horses. Difficult light settings, such as direct sunlight or darkness, or very even-coloured coats, complicate the precise placement of reference points. The analysis and detection with software-generated movement trajectories using pose estimation is very promising but requires further development. 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 group, one analysis group of fore and hindlimb lame horses and a control group of sound horses. The first group was used to train the network;afterwards, horses with and without lameness were evaluated. The results show that forelimb lameness can be detected by visualising the trajectories of the reference points on the head and both forelimbs. In hindlimb lameness, the stifle showed promising results as a reference point, whereas the tuber coxae were deemed unsuitable as a reference point. The study presents a feasible application of pose estimation for lameness detection, but further development using a larger dataset is essential

Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis

Background 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. Objectives A software tool was developed to aid in the diagnosis of equine ophthalmic diseases, especially uveitis. Study design Prospective comparison of software and clinical diagnoses. Methods A deep learning approach for image classification was used to train software by analysing photographs of equine eyes to make a statement on whether the horse was displaying signs of uveitis or other ophthalmic diseases. Four basis networks of different sizes (MobileNetV2, InceptionV3, VGG16, VGG19) with modified top-layers were evaluated. Convolutional Neural Networks (CNN) were trained on 2346 pictures of equine eyes, which were augmented to 9384 images. 261 separate unmodified images were used to evaluate the performance of the trained network. Results Cross validation showed accuracy of 99.82% on training data and 96.66% on validation data when distinguishing between three categories (uveitis, other ophthalmic diseases, healthy). Main limitations One source of selection bias for the artificial intelligence presumably was the increased pupil size, which was mainly present in horses with ophthalmic diseases due to the use of mydriatics, and was not homogeneously dispersed in all categories of the dataset. Conclusions Our system for detection of equine uveitis is unique and novel and can differentiate between uveitis and other equine ophthalmic diseases. Its development also serves as a proof-of-concept for image-based detection of ophthalmic diseases in general and as a basis for its further use and expansion

Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases

Background/Objectives 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. Study design 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 equine ophthalmic diseases. The same data set was shown to different groups of veterinarians (equine, small animal, mixed practice, other) using an opinion poll to compare the results and evaluate the performance of the programme. Convolutional Neural Networks (CNN) were trained on 2346 photographs of equine eyes, which were augmented to 9384 images. Two hundred and sixty-one separate unmodified images were used to evaluate the trained network. The trained deep learning tool was used on 40 photographs of equine eyes (10 healthy, 12 uveitis, 18 other diseases). An opinion poll was used to evaluate the diagnostic performance of 148 veterinarians in comparison to the software tool. Results The probability for the correct answer was 93% for the AI programme. Equine veterinarians answered correctly in 76%, whereas other veterinarians reached 67% probability for the correct diagnosis. Main limitations Diagnosis was solely based on images of equine eyes without the possibility to evaluate the inner eye. Conclusions The deep learning tool proved to be at least equivalent to veterinarians in assessing ophthalmic diseases in photographs. We therefore conclude that the software tool may be useful in detecting potential emergency cases. In this context, blindness in horses may be prevented as the horse can receive accurate treatment or can be sent to an equine hospital. Furthermore, the tool gives less experienced veterinarians the opportunity to differentiate between uveitis and other ocular anterior segment disease and to support them in their decision-making regarding treatment