Do tight nosebands have an effect on the upper airways of horses?

BACKGROUND/OBJECTIVES: The public perception relating to the welfare of horses involved with equestrian sports is associated with training methods used and the presentation of horses at events. In this context, very tight nosebands, which are intended to prevent the horse from opening its mouth, also attract a lot of attention. Various studies have evaluated the impact of tight nosebands on stress parameters, whereas the effect of tight nosebands on upper airway function is unknown. Therefore, the aim of the study was to use overground endoscopy to evaluate changes in pharyngeal and laryngeal function when a tight noseband is fitted. Moreover, the ridden horse pain ethogram (RHpE) was applied to investigate signs of discomfort (Dyson et al., 2018). STUDY DESIGN: A randomized, blinded, and prospective study was performed. METHODS: Sixteen warmblood horses consisting of twelve mares and four geldings with a mean age of 11.63 ± 3.53 years were ridden on 2 consecutive days with either loose or tight nosebands (two fingers or no space between bridge of the nose and noseband, respectively) and inserted endoscope in a random order. Videos were taken in a riding arena during a standardized exercise protocol involving beginner level tasks for 30 min in all gaits. For video analysis, freeze frames were prepared and analyzed at the beginning of the expiration phase. Pharyngeal diameter was measured using the pharynx-epiglottis ratio. Other findings (swallowing, pharyngeal collapse, soft palate movements, and secretion) were also evaluated. Moreover, the RHpE was applied. Descriptive statistics and generalized linear mixed effects models were used. Results with a p-value < 0.05 were considered statistically significant. RESULTS: While the pharynx-epiglottis ratio did not change significantly in horses ridden with loose versus tight nosebands, there was an increase in mean grade and total counts of parameters assessed in the pharyngeal region, for example, grade of secretion (1.5 [±SD 0.89] vs. 3.13 [±SD 0.96]; p = 0.0001), axial deviation of the aryepiglottic folds (0.29 [±SD 0.73] vs. 1.33 [±SD 1.44]; p = 0.01), and pharyngeal collapse (0.69 [±SD 0.87] vs. 1.88 [±SD 1.54]; p = 0.005) in horses ridden with tight nosebands. There was no RHpE score above 8 indicating musculoskeletal pain, but the RHpE scores were significantly higher in horses ridden with tight nosebands (p < 0.001). MAIN LIMITATIONS: Video quality was limited when horses showed large amounts of secretion. Another limitation was the small number of horses. CONCLUSIONS: Results add to the evidence obtained in other studies that tight nosebands do not only cause adverse reactions based on the RHpE score such as head behind the vertical or intense staring but also contribute to changes in the pharyngeal region, such as increased secretion and collapse of pharyngeal structures. This may provide further support for future decisions regarding regulations on nosebands.

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.