Physical examination and CT to assess thoracic injury in 137 cats presented to UK referral hospitals after trauma.

OBJECTIVES: The aim of the study was to describe clinical examination and thoracic CT (TCT) findings in cats after trauma, and to identify physical examination findings associated with both abnormalities on TCT and the need for therapeutic interventions. METHODS: A multicentre, retrospective, observational study was conducted. Cats admitted to the participating hospitals with a history of blunt trauma and that underwent TCT were eligible. Data were collected on signalment, history, physical examination, TCT findings and subsequent interventions. RESULTS: In total, 137 cats were included. Road traffic accidents (RTAs) were the most frequently reported cause of trauma (69%). Tachypnoea (32%), pale mucous membranes (22%) and dyspnoea (20%) were the most common abnormal findings on thoracic examination. The most frequently identified thoracic pathologies on TCT were atelectasis (34%), pulmonary contusions (33%), pneumothorax (29%) and pleural effusion (20%). Thoracocentesis was the most commonly performed intervention (12%), followed by chest drain placement (7%). A total of 45 (33%) cats had no physical examination abnormalities but did have abnormalities detected on TCT; six of these cats required interventions. Increasing numbers of thoracic abnormalities on clinical examination were associated with increasing likelihood of having abnormal findings on TCT (odds ratio [OR] 2.04, 95% confidence interval [CI] 1.21-3.44, P = 0.008) and of requiring an intervention (OR 1.82, 95% CI 1.32-2.51, P <0.001). CONCLUSIONS AND RELEVANCE: RTAs were the most common reported cause of blunt trauma. Atelectasis, pulmonary contusions and pneumothorax were the most common abnormalities identified on TCT, and thoracic drainage was the most utilised intervention. TCT may be useful in identifying cats with normal thoracic physical examination findings that have significant thoracic pathology, and a high number of abnormal findings on thoracic examination should raise suspicion for both minor and major thoracic pathology. The results of this study can be used to assist in selecting appropriate cases for TCT after blunt trauma.

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep.

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.