Standing equine cheek tooth extraction: A multivariate analysis of the effect of antibiotics on the risk of post-operative complications.

BACKGROUND: Commonly, cheek tooth extraction performed in standing horses using perioperative prophylactic antibiotics, results in low post-operative complication rates. However, no studies have documented the relevance of perioperative antibiotics to the risk of post-operative complications. OBJECTIVES: To examine the association between perioperative antibiotics and post-operative complications after standing cheek tooth extraction. STUDY DESIGN: Retrospective cohort study. METHODS: Information from clinical records and follow-up questionnaires relating to horses subjected to cheek tooth extractions between September 2016 and May 2020 was obtained. Post-operative complications and associations with perioperative antibiotics, age, sex, breed, diagnosis, tooth position, and extraction method were analysed using multivariate logistic regression. RESULTS: A total of 305 horses were included, and of these 71 (23.3%) received perioperative antibiotics. Antibiotics were not associated with the risk of complications in 264 horses that underwent standard oral extraction; 9/49 (18.4%) that received antibiotics and 35/215 (16.3%) that did not receive antibiotics experienced postoperative complications (P = 1, RR = 0.89, OR = 1, OR CI = [0.41; 2.46]). Of 41 horses that had cheek tooth extraction through minimally invasive transbuccal cheek tooth extraction (MTE), 5/22 (22.7%) that received antibiotics and 10/19 (52.6%) that did not receive antibiotics, experienced postoperative complications. Although not statistically significant when adjusting for multiple comparisons (naïve P = 0.04, adjusted P = 0.26, RR = 2.32, OR = 4.48, OR CI = [1.05; 19.11]), this finding is clinically relevant. Younger age was also significantly associated with development of complications (P = 0.02, OR = 0.92 per year, OR CI = [0.87; 1.36]). MAIN LIMITATIONS: The retrospective nature of the study leads to uncontrollable potential confounders and there is a relatively low number of MTE cases. CONCLUSION: Perioperative antibiotics were not associated with a lower complication rate in horses subjected to standard standing cheek tooth extraction. Use of perioperative antibiotics in conjunction with MTE may be merited, although further investigations are needed.

Automatic monitoring and detection of tail-biting behavior in groups of pigs using video-based deep learning methods.

Automated monitoring of pigs for timely detection of changes in behavior and the onset of tail biting might enable farmers to take immediate management actions, and thus decrease health and welfare issues on-farm. Our goal was to develop computer vision-based methods to detect tail biting in pigs using a convolutional neural network (CNN) to extract spatial information, combined with secondary networks accounting for temporal information. Two secondary frameworks were utilized, being a long short-term memory (LSTM) network applied to sequences of image features (CNN-LSTM), and a CNN applied to image representations of sequences (CNN-CNN). To achieve our goal, this study aimed to answer the following questions: (a) Can the methods detect tail biting from video recordings of entire pens? (b) Can we utilize principal component analyses (PCA) to reduce the dimensionality of the feature vector and only use relevant principal components (PC)? (c) Is there potential to increase performance in optimizing the threshold for class separation of the predicted probabilities of the outcome? (d) What is the performance of the methods with respect to each other? The study utilized one-hour video recordings of 10 pens with pigs prior to weaning, containing a total of 208 tail-biting events of varying lengths. The pre-trained VGG-16 was used to extract spatial features from the data, which were subsequently pre-processed and divided into train/test sets before input to the LSTM/CNN. The performance of the methods regarding data pre-processing and model building was systematically compared using cross-validation. Final models were run with optimal settings and evaluated on an independent test-set. The proposed methods detected tail biting with a major-mean accuracy (MMA) of 71.3 and 64.7% for the CNN-LSTM and the CNN-CNN network, respectively. Applying PCA and using a limited number of PCs significantly increased the performance of both methods, while optimizing the threshold for class separation did result in a consistent but not significant increase of the performance. Both methods can detect tail biting from video data, but the CNN-LSTM was superior in generalizing when evaluated on new data, i.e., data not used for training the models, compared to the CNN-CNN method.

Prediction of Tail Biting Events in Finisher Pigs from Automatically Recorded Sensor Data.

Tail biting in pigs is an animal welfare problem, and tail biting should be prevented from developing into tail damage. One strategy could be to predict events of tail biting so that the farmer can make timely interventions in specific pens. In the current investigation, sensor data on water usage (water flow and activation frequency) and pen temperature (above solid and slatted floor) were included in the development of a prediction algorithm for tail biting. Steps in the development included modelling of data sources with dynamic linear models, optimisation and training of artificial neural networks and combining predictions of the single data sources with a Bayesian ensemble strategy. Lastly, the Bayesian ensemble combination was tested on a separate batch of finisher pigs in a real-life setting. The final prediction algorithm had an AUC > 0.80, and thus it does seem possible to predict events of tail biting from already available sensor data. However, around 30% of the no-event days were false alarms, and more event-specific predictors are needed. Thus, it was suggested that farmers could use the alarms to point out pens that need greater attention.