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.

Summary: 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.

Stress has a significant impact on equine welfare. There are some studies on the stress response in horses ridden with tight nosebands, but little is known about other stress parameters than cortisol, which potentially could address an emotional component. In this study, blood samples of a total of 74 warmblood horses were used to establish reference values for plasma substance P (SP) concentrations. Moreover, 16 of these warmblood horses were included in a stress model. Four different stress levels (level 1: horses ridden with loose noseband, level 2: tight noseband, level 3: loose noseband and overground endoscope, level 4: tight noseband and overground endoscope) were applied to evaluate SP as a potential stress parameter in horses. Blood samples were taken at rest (t0) and directly after inducing stress (noseband tightening, insertion of endoscope; t1), as well as after 20 min of riding at all gaits (t2). A ridden horse ethogram was applied and showed that horses in the tight noseband group resorted to other stress-related behavioral issues than horses with loose nosebands. Serum cortisol showed a linear increase concurrent with the increase in stress levels with a significant difference between level 1 and level 4 (p = 0.043), proving that stress factors were adequate to evaluate the stress response, whereas SP did not show a correlation with the stress levels. Furthermore, concentrations of SP differed widely between horses but stayed within more narrow limits in the individual horse. As a conclusion, SP might not be a reliable stress parameter in horses in the applied minor stress model.

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.

During hospitalization horses may develop gastrointestinal conditions triggered by a stress-associated weak local immune system. The prospective, clinical trial was conducted to find out whether fecal immunoglobulin A (IgA) concentrations could be determined in hospitalized horses and how they changed during hospitalization and in response to various stressors. Samples were obtained from 110 horses and a control group (n = 14). At arrival in the hospital, horses were categorized into pain grades (1-5), and elective versus strenuous surgery (> 2 hours, traumatic and emergency procedures). Feces were collected on day 1, day 2, day 3, and day 7 in all horses. Blood samples were obtained at the same intervals, but additionally after general anaesthesia in horses undergoing surgery (day 2). IgA concentration in feces was determined by ELISA and measured in optical density at 450nm. The control group showed constant IgA concentrations on all days (mean value 0.30 OD₄₅₀ ±SD 0.11, 1.26 mg/g; n = 11). After general anaesthesia fecal IgA concentrations decreased considerably independent of duration and type of surgery (P < 0.001 for elective and P = 0.043 for traumatic surgeries). High plasma cortisol concentrations were weakly correlated with low fecal IgA on the day after surgery (P = 0.012, day 3, correlation coefficient r = 0.113). Equine fecal IgA concentrations showed a decline associated with transport, surgery, and hospitalization in general, indicating that stress has an impact on the local intestinal immune function and may predispose horses for developing gastrointestinal diseases such as enterocolitis.

Background: While there is agreement that horses experience pain during the actual headshaking attacks, there is still a lack of research about the time between the individual episodes in this species. Objectives: To evaluate the signs of pain in horses with TMHS at rest in the absence of common triggering factors. Animals: Twenty TMHS horses (not head shaking at the time of pain assessment), 20 horses with colic, and 20 clinically healthy horses. Methods: Descriptive study assessing pain in TMHS horses during the absence of clinical signs using the Horse Grimace scale (HGS). To grade the pain shown with TMHS, horses with moderate gastrointestinal pain conditions and a healthy horses' group were also included. For evaluation, videos were taken on two consecutive days from both sides of the face (healthy, TMHS) or before analgesics were applied (colic). Eight photos per horse were extracted from the videos, randomized, and evaluated by three examiner groups, each comprising two persons: (a) last-year veterinary medicine students without previous use of the HGS, (b) experienced veterinarians without experience in using the HGS, and (c) experienced veterinarians who regularly apply the HGS. All examiners were blinded to the underlying condition. Results: Median HGS scores in the groups (healthy, gastrointestinal, TMHS) differed significantly from each other (p < 0.05). Healthy horses had median HGS scores below 2 (median 1.2, range: 0.1–2.4), whereas TMHS horses had higher median HGS scores (5.6, range 2.4–7.4), and colic horses had the highest HGS scores (6.6, range: 3.8–8.4). There was a statistically significant difference for all facial action units (FAU) when the different horse groups were compared (p < 0.05). Effect sizes were large for the healthy horses' group (ES-1.23) and for the colic horses (ES 0.86), and small to medium for the TMHS horses (ES 0.37). Clinical Importance: The results of this study show that pain occurs between individual TMHS attacks in severely affected horses and that the quality of life of these horses must be questioned and evaluated individually.