Feasibility of the use of deep learning classification of teat-end condition in Holstein cattle

Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests

Machine learning algorithms have been applied to various animal husbandry and veterinary-related problems; however, its use in Johne's disease diagnosis and control is still in its infancy. The following proof-of-concept study explores the application of tree-based (decision trees and random forest) algorithms to analyze repeat milk testing data from 1197 Canadian dairy cows and the algorithms' ability to predict future Johne's test results. The random forest models using milk component testing results alongside past Johne's results demonstrated a good predictive performance for a future Johne's ELISA result with a dichotomous outcome (positive vs. negative). The final random forest model yielded a kappa of 0.626, a roc AUC of 0.915, a sensitivity of 72%, and a specificity of 98%. The positive predictive and negative predictive values were 0.81 and 0.97, respectively. The decision tree models provided an interpretable alternative to the random forest algorithms with a slight decrease in model sensitivity. The results of this research suggest a promising avenue for future targeted Johne's testing schemes. Further research is needed to validate these techniques in real-world settings and explore their incorporation in prevention and control programs.

Antibiograms, multidrug resistance, and milk-related parameters of bacteria isolated from milk of dairy cattle in Phatthalung, Thailand

Background and Aim

Milk, a nutritious food, is widely consumed in human diets; however, contamination by micro-organisms can negatively impact its quality and consumer health. Contamination by micro-organisms affects the quality of milk, which can affect the quality of the milk production chain. This study aimed to determine the changes in milk composition and antibiotic susceptibility related to bacteria isolated from dairy cow milk.

Materials and Methods

Raw milk samples were collected from 72 dairy cows. All milk samples were subjected to the California Mastitis Test (CMT) for CMT score determination. We also investigated milk composition, bacterial culture (BC), and antibiotic susceptibility.

Results

About 47.22% and 30.56% of dairy cattle were positive for CMT + BC and automatic somatic cell count (ASCC) + BC, respectively. Fecal appearance and animal age were found to be risk factors for ASCC + BC positivity in dairy cattle. Bacteria were found in approximately 76% of milk samples, with the most common isolated species being α-hemolytic Streptococcus spp., coagulase-negative Staphylococcus spp., and Escherichia coli. Of these, 70% are resistant to at least one antibiotic. Variation in the multidrug resistance pattern was high in Klebsiella spp.

Conclusions

Fecal appearance and animal age are risk factors for ASCC + BC positivity in dairy cattle. This study identified antibiotic and multidrug resistance patterns, which require comprehensive studies and effective surveillance systems. Remarkably, the use of antibiotic therapy in dairy cattle should be monitored.

Ultrasound examination of the teat parameters of mastitis and healed udder quarters

A healthy udder is important for economical and hygienic milk production. In this study we determined the teat parameters, measured during milking, in the presence of mastitis and after its recovery. The length of the streak canal, the area of the teat end and the area of the pars papillaris of 52 Holstein-Friesian cows were examined by ultrasound before, immediately after, and two hours after milking. Pars papillaris size before milking was higher in the presence of disease (P = 0.011), but was larger before and two hours after milking (P = 0.047 and P = 0.024) with more severe mastitis. During mastitis and recovery, the dimensions of the streak canal (P=0.056-0.934) and teat end (P=0.156-0.420) did not differ from each other. The obtained results show that during mastitis, coagulated milk dilates the pars papillaris better (P=0.011) than the higher milk production of the cured cow. After more severe mastitis, regeneration of the pars papillaris as well as post-milking regeneration also takes place more slowly. The size and size changes of the streak canal and teat end are not affected by the presence or healing of the udder inflammation.

Artificial intelligence in veterinary diagnostic imaging: A literature review

Artificial intelligence in veterinary medicine is an emerging field. Machine learning, a subfield of artificial intelligence, allows computer programs to analyze large imaging datasets and learn to perform tasks relevant to veterinary diagnostic imaging. This review summarizes the small, yet growing body of artificial intelligence literature in veterinary imaging, provides necessary background to understand these papers, and provides author commentary on the state of the field. To date, less than 40 peer-reviewed publications have utilized machine learning to perform imaging-associated tasks across multiple anatomic regions in veterinary clinical and biomedical research. Major challenges in this field include collection and cleaning of sufficient image data, selection of high-quality ground truth labels, formation of relationships between veterinary and machine learning professionals, and closure of the gap between academic uses of artificial intelligence and currently available commercial products. Further development of artificial intelligence has the potential to help meet the growing need for radiological services through applications in workflow, quality control, and image interpretation for both general practitioners and radiologists.

Unsupervised Few Shot Key Frame Extraction for Cow Teat Videos

A novel method of monitoring the health of dairy cows in large-scale dairy farms is proposed via image-based analysis of cows on rotary-based milking platforms, where deep learning is used to classify the extent of teat-end hyperkeratosis. The videos can be analyzed to segment the teats for feature analysis, which can then be used to assess the risk of infections and other diseases. This analysis can be performed more efficiently by using the key frames of each cow as they pass through the image frame. Extracting key frames from these videos would greatly simplify this analysis, but there are several challenges. First, data collection in the farm setting is harsh, resulting in unpredictable temporal key frame positions; empty, obfuscated, or shifted images of the cow’s teats; frequently empty stalls due to challenges with herding cows into the parlor; and regular interruptions and reversals in the direction of the parlor. Second, supervised learning requires expensive and time-consuming human annotation of key frames, which is impractical in large commercial dairy farms housing thousands of cows. Unsupervised learning methods rely on large frame differences and often suffer low performance. In this paper, we propose a novel unsupervised few-shot learning model which extracts key frames from large (∼21,000 frames) video streams. Using a simple L1 distance metric that combines both image and deep features between each unlabeled frame and a few (32) labeled key frames, a key frame selection mechanism, and a quality check process, key frames can be extracted with sufficient accuracy (F score 63.6%) and timeliness (<10 min per 21,000 frames) for commercial dairy farm setting demands.

Separable Confident Transductive Learning for Dairy Cows Teat-End Condition Classification

Simple Summary

The health of dairy cows is important for milk quality and the health of the mammary gland. Traditionally, teat-end health has been assessed manually through visual inspection of teat-end callosity thickness and roughness (i.e., hyperkeratosis), which is a risk-factor for mastitis. Here, we describe a computer-vision approach to replace the time-consuming and expensive manual assessment of teat-end hyperkeratosis. Using separable confident transductive learning, a convolutional neural network is trained with the goal of increasing the feature differences in the images of teat-ends with different classifications of hyperkeratosis. When compared with the traditional approach of transfer learning of a convolution neural network for classifying the extent of hyperkeratosis, the overall accuracy of our model increased from 61.8 to 77.6%. This substantial improvement in accuracy renders the possibility of using image-based machine learning to routinely monitor hyperkeratosis on commercial dairy farm settings.

Abstract

Teat-end health assessments are crucial to maintain milk quality and dairy cow health. One approach to automate teat-end health assessments is by using a convolutional neural network to classify the magnitude of teat-end alterations based on digital images. This approach has been demonstrated as feasible with GoogLeNet but there remains a number of challenges, such as low performance and comparing performance with different ImageNet models. In this paper, we present a separable confident transductive learning (SCTL) model to improve the performance of teat-end image classification. First, we propose a separation loss to ameliorate the inter-class dispersion. Second, we generate high confident pseudo labels to optimize the network. We further employ transductive learning to narrow the gap between training and test datasets with categorical maximum mean discrepancy loss. Experimental results demonstrate that the proposed SCTL model consistently achieves higher accuracy across all seventeen different ImageNet models when compared with retraining of original approaches.

The unmet potential of artificial intelligence in veterinary medicine

Veterinary medicine is a broad and growing discipline that includes topics such as companion animal health, population medicine and zoonotic diseases, and agriculture. In this article, we provide insight on how artificial intelligence works and how it is currently applied in veterinary medicine. We also discuss its potential in veterinary medicine. Given the rapid pace of research and commercial product developments in this area, the next several years will pose challenges to understanding, interpreting, and adopting this powerful and evolving technology. Artificial intelligence has the potential to enable veterinarians to perform tasks more efficiently while providing new insights for the management and treatment of disorders. It is our hope that this will translate to better quality of life for animals and those who care for them.