Automated prediction of mastitis infection patterns in dairy herds using machine learning

© 2020, The Author(s). Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in dairy cattle. A critical step in the prevention of mastitis is the diagnosis of the predominant route of transmission of pathogens into either contagious (CONT) or environmental (ENV), with environmental being further subdivided as transmission during either the nonlactating “dry” period (EDP) or lactating period (EL). Using data from 1000 farms, random forest algorithms were able to replicate the complex herd level diagnoses made by specialist veterinary clinicians with a high degree of accuracy. An accuracy of 98%, positive predictive value (PPV) of 86% and negative predictive value (NPV) of 99% was achieved for the diagnosis of CONT vs ENV (with CONT as a “positive” diagnosis), and an accuracy of 78%, PPV of 76% and NPV of 81% for the diagnosis of EDP vs EL (with EDP as a “positive” diagnosis). An accurate, automated mastitis diagnosis tool has great potential to aid non-specialist veterinary clinicians to make a rapid herd level diagnosis and promptly implement appropriate control measures for an extremely damaging disease in terms of animal health, productivity, welfare and antimicrobial use

MALDI-TOF mass spectrometry profiling of bovine skim milk for subclinical mastitis detection

INTRODUCTION: Mastitis is one of most impacting health issues in bovine dairy farming that reduces milk yield and quality, leading to important economic losses. Subclinical forms of the disease are routinely monitored through the measurement of somatic cell count (SCC) and microbiological tests. However, their identification can be tricky, reducing the possibilities of early treatments. In this study, a MALDI-TOF mass spectrometry approach was applied to milk samples collected from cows classified according to the SCC, to identify differences in polypeptide/protein profiles. MATERIALS AND METHODS: Twenty-nine raw milk samples with SCC >200,000 cell/ml (group H) and 91 samples with SCC lower than 200,000 (group L) were randomly collected from 12 dairy farms. Spectral profiles from skim milk were acquired in the positive linear mode within the 4,000–20,000 m/z mass acquisition range. RESULTS AND DISCUSSION: Based on signal intensity, a total of 24 peaks emerged as significant different between the two groups. The most discriminant signals (4,218.2 and 4,342.98 m/z) presented a ROC curve with AUC values higher than 0.8. Classification algorithms (i.e., quick classifier, genetic algorithm, and supervised neural network) were applied for generating models able to classify new spectra (i.e., samples) into the two classes. Our results support the MALDI-TOF mass spectrometry profiling as a tool to detect mastitic milk samples and to potentially discover biomarkers of the disease. Thanks to its rapidity and low-cost, such method could be associated with the SCC measurement for the early diagnosis of subclinical mastitis

LSTM Models to Support the Selective Antibiotic Treatment Strategy of Dairy Cows in the Dry Period

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceUdder inflammation, known as mastitis, is the most significant disease of dairy cows worldwide, invoking substantial economic losses. The current common strategy to reduce this problem is the prophylactic administration of antibiotics treatment of cows during their dry period. Paradoxically, the indiscriminate use of antibiotics in animals and humans has been the leading cause of antimicrobial resistance, a concern in several public health organizations. In light of these assumptions, at the beginning of 2022, the European Union made it illegal to routinely administer antibiotics on farms, with Regulation 2019/6 of 11 December 2018. Considering this new scenario, the objective of this study was to produce a model that supports the decisions of veterinarians when administering antibiotics in the dry period of dairy cows. Deep learning models were used, namely LSTM layers that operate with dynamic features from milk recordings and a dense layer that uses static features. Two approaches were chosen to deal with this problem. The first is based on a binary classification model that considers the occurrence of mastitis within 60 days after calving. The second approach was a multiclass classification model based on veterinary expert judgment. In each approach, three models were implemented, a Vanilla LSTM, a Stacked LSTM, and a Stacked LSTM with a dense layer working in parallel. The best performances from binary and multiclass approaches were 65% and 84% accuracy, respectively. It was possible to conclude that the models of the multiclass classification approach had better performance than the other classification. The capture of long- and short-term dependencies in the LSTM models, especially with the combination of static features, obtained promising results, which will undoubtedly contribute to producing a machine learning system with a prompt and affordable response, allowing for a reduction in the administration of antibiotics in dairy cows to the strictly necessary

Assessment of associations between transition diseases and reproductive performance of dairy cows using survival analysis and decision tree algorithms

This study aimed to evaluate the associations between transition cow conditions and diseases TD with fertility in Holstein cows, and to compare analytic methods for doing so. Kaplan-Meier, Cox proportional hazard, and decision tree models were used to analyze the associations of TD with the pregnancy risk at 120 and 210 DIM from a 1-year cohort with 1946 calvings from one farm. The association between TD and fertility was evaluated as follows: 1 cows with TD whether complicated with another TD or not TD-all, versus healthy cows, and 2 cows with uncomplicated TD TD-single, versus cows with multiple TD TD+; complicated cases, versus healthy cows. The occurrence of twins, milk fever, retained placenta, metritis, ketosis, displaced abomasum, and clinical mastitis were recorded. Using Kaplan-Meier models, in primiparous cows the 120 DIM pregnancy risk was 62% (95% CI: 57-67 %) for healthy animals. This was not significantly different for TD-single (58%; 95% CI: 51-66 %) but was reduced for TD+ (45%; 95% CI: 33-60 %). Among healthy primiparous cows, 80% (95% CI: 75-84 %) were pregnant by 210 DIM, but pregnancy risk at that time was reduced for primiparous cows with TD-single (72%; 95% CI: 65-79 %) and TD+ (62%; 95% CI: 49-75 %). In healthy multiparous cows, the 120 DIM pregnancy risk was 53% (95% CI: 49-56 %), which was reduced for TD-single (36%; 95% CI: 31-42 %) and TD+ (30%; 95% CI: 24-38 %). The 210 DIM pregnancy risk for healthy multiparous cows was 70% (95% CI: 67-72 %), being higher than the 210 DIM pregnancy risk for multiparous cows with TD-single (47%; 95% CI: 42-53 %) or TD+ (46%; 95% CI: 38-54 %). Cows with TD-all presented similar pregnancy risk estimates as for TD+. Cox proportional hazards regressions provided similar magnitudes of effects as the Kaplan-Meier estimates. Survival analysis and decision tree models identified parity as the most influential variable affecting fertility. Both modeling techniques concurred that TD + had a greater effect than TD-single on the probability of pregnancy at 120 and 210 DIM. Decision trees for individual TD identified that displaced abomasum affected fertility at 120 DIM in primiparous while metritis was the most influential TD at 120 and 210 DIM for multiparous cows. The data were too sparse to assess multiple interactions in multivariable Cox proportional hazard models for individual TD. Machine learning helped to explore interactions between individual TD to study their hierarchical effect on fertility, identifying conditional relationships that merit further investigation

Data-based approaches to improve accuracy and timing of mastitis detection in automatic milking systems

Bovine mastitis is the inflammation of the entire udder or individual mammary glands. The pain associated with mastitis is a serious welfare issue, and the negative effects on milk production and quality cost millions of dollars to the Australian dairy industry every year. Automatic milking systems (AMS) are becoming increasingly popular to minimise labour and labour cost without compromising milk production. Because of lower farmer-cow contact in AMS, farmers or herd managers are dependent on the AMS-incorporated inline sensors for automatic mastitis detection. The International Organization for Standardization (ISO) recommended at least 70% sensitivity (Se) and at least > 99% specificity (Sp) for automatic detection of abnormal milk and currently there are gaps to achieve this ISO-standard by the inline sensors. Hence, we developed and implemented a research program with the overarching goal of improving the accuracy and timing of mastitis detection in AMS by using multiple sources of inline sensor-derived information related to milking, and also animal behavioural changes. The research was largely based on Se and Sp of mastitis detection and quarter-level inline sensor data in AMS. The literature review (Chapter 2) identified the current knowledge gaps and the opportunities to improve the Se and Sp of mastitis detection through new and innovative data-based research in AMS. The electrical conductivity (EC) inline sensor data analysis (3-year historic database) focussed on developing new indexes from the available EC data to fulfil ISO standard (Chapter 3). Chapter 3 concluded that EC data alone cannot provide the required accuracy to detect infected quarters, leading us to hypothesise that by incorporating other data, early detection of mastitis in AMS herds could be improved. Moreover, the sensitivity of the EC measuring sensor could also be improved by measuring the most informative milk samples like strict foremilk, which is currently discarded in AMS (Chapter 4). We found that foremilk sampled before milk ejection was more sensitive for detection of mastitis than foremilk harvested after milk ejection, and that indicators like lactate dehydrogenase (LDH) have potential to differentiate mastitis originated from Gram-negative versus Gram-positive pathogens. The hypothesis that multiple milking-related inline sensor data (e.g., milk yield, milk flow rate, number of incomplete milkings) provided better Se and Sp than single inline sensor data was tested in the study reported in Chapter 5. This study demonstrated that by combining multiple measurements with adequate statistical models, mastitis status prediction can be improved. In addition, behavioural changes such as daily activity and daily rumination time captured by activity and rumination sensors (SRC collars) were also useful for better mastitis prediction when combined with EC data (Chapter 6). In summary, better mastitis detection is possible by looking at multiple inline sensor data as well as animal behavioural changes. This thesis provides innovative approaches and scientifically-based possibilities to utilise multiple sources of data for improvement of the Se and Sp of automatic mastitis detection in AMS in the future. The research makes original and innovative contributions to knowledge and sets the basis for future integration of its findings and models into practical tools for herd managers

Management and technology solutions for improving milk quality

Mastitis is one of the most common and expensive dairy cattle diseases. Mastitis prevention and management are key factors in herd health and improved milk quality. One objective of this research was to evaluate management solutions to maintain a low somatic cell count, based on survey responses from Kentucky dairy producers. Because hyperkeratosis may increase mastitis incidence, another objective of this research was to examine changes in teat end hyperkeratosis in a herd transitioning from a standard pulsation milking system to an individual quarter pulsation milking system. The last objective of this research was to evaluate technologies that monitored rumination time, neck activity, reticulorumen temperature, and milk yield as potential mastitis detection devices

Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine

Background: Recent focus on earlier detection of pathogen introduction in human and animal populations has led to the development of surveillance systems based on automated monitoring of health data. Real- or near real-time monitoring of pre-diagnostic data requires automated classification of records into syndromes-syndromic surveillance-using algorithms that incorporate medical knowledge in a reliable and efficient way, while remaining comprehensible to end users. Methods: This paper describes the application of two of machine learning (Naïve Bayes and Decision Trees) and rule-based methods to extract syndromic information from laboratory test requests submitted to a veterinary diagnostic laboratory. Results: High performance (F1-macro = 0.9995) was achieved through the use of a rule-based syndrome classifier, based on rule induction followed by manual modification during the construction phase, which also resulted in clear interpretability of the resulting classification process. An unmodified rule induction algorithm achieved an F1-micro score of 0.979 though this fell to 0.677 when performance for individual classes was averaged in an unweighted manner (F1-macro), due to the fact that the algorithm failed to learn 3 of the 16 classes from the training set. Decision Trees showed equal interpretability to the rule-based approaches, but achieved an F1-micro score of 0.923 (falling to 0.311 when classes are given equal weight). A Naïve Bayes classifier learned all classes and achieved high performance (F1-micro = 0.994 and F1-macro =. 955), however the classification process is not transparent to the domain experts. Conclusion: The use of a manually customised rule set allowed for the development of a system for classification of laboratory tests into syndromic groups with very high performance, and high interpretability by the domain experts. Further research is required to develop internal validation rules in order to establish automated methods to update model rules without user input

Implementation of machine learning for the evaluation of mastitis and antimicrobial resistance in dairy cows

Bovine mastitis is one of the biggest concerns in the dairy industry, where it affects sustainable milk production, farm economy and animal health. Most of the mastitis pathogens are bacterial in origin and accurate diagnosis of them enables understanding the epidemiology, outbreak prevention and rapid cure of the disease. This thesis aimed to provide a diagnostic solution that couples Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectroscopy coupled with machine learning (ML), for detecting bovine mastitis pathogens at the subspecies level based on their phenotypic characters. In Chapter 3, MALDI-TOF coupled with ML was performed to discriminate bovine mastitis-causing Streptococcus uberis based on transmission routes; contagious and environmental. S. uberis isolates collected from dairy farms across England and Wales were compared within and between farms. The findings of this chapter suggested that the proposed methodology has the potential of successful classification at the farm level. In Chapter 4, MALDI-TOF coupled with ML was performed to show proteomic differences between bovine mastitis-causing Escherichia coli isolates with different clinical outcomes (clinical and subclinical) and disease phenotype (persistent and non-persistent). The findings of this chapter showed that phenotypic differences can be detected by the proposed methodology even for genotypically identical isolates. In Chapter 5, MALDI-TOF coupled with ML was performed to differentiate benzylpenicillin signatures of bovine mastitis-causing Staphylococcus aureus isolates. The findings of this chapter presented that the proposed methodology enables fast, affordable and effective diag-nostic solution for targeting resistant bacteria in dairy cows. Having shown this methodology successfully worked for differentiating benzylpenicillin resistant and susceptible S. aureus isolates in Chapter 5, the same technique was applied to other mastitis agents Enterococcus faecalis and Enterococcus faecium and for profiling other antimicrobials besides benzylpenicillin in Chapter 6. The findings of this chapter demonstrated that MALDI-TOF coupled with ML allows monitoring the disease epidemiology and provides suggestions for adjusting farm management strategies. Taken together, this thesis highlights that MALDI-TOF coupled with ML is capable of dis-criminating bovine mastitis pathogens at subspecies level based on transmission route, clinical outcome and antimicrobial resistance profile, which could be used as a diagnostic tool for bo-vine mastitis at dairy farms