Detection Of Clinical Mastitis Research Articles

Decision-tree induction to detect clinical mastitis with automatic milking

This study explored the potential of using decision-tree induction to develop models for the detection of clinical mastitis with automatic milking. Sensor data (including electrical conductivity and colour) of over 711,000 quarter milkings were collected from December 2006 till August 2007 at six Dutch dairy herds milking automatically. Farmer recordings of quarter milkings with visible signs of mastitis were considered as gold standard positive cases ( n = 97), quarter milkings that were recorded as being visually normal as gold standard negatives ( n = 339). Randomly chosen quarter milkings that were not visually checked, that were outside a 2-week range before or after a gold standard positive case and that were not manually or automatically separated were added to end up with 3000 gold standard negatives. Decision trees, with varying confidence factors and cost matrices to study their effect on performance characteristics, were developed with the probability of having clinical mastitis for each quarter milking as output. Detection performance of decision trees was estimated using 10-fold cross-validation. Evaluated performance characteristics were the sensitivity and specificity, both calculated at a threshold value of 0.50 for the probability estimate for clinical mastitis. The transformed partial area under the curve was used to summarise the diagnostic ability of decision trees within a specified range of interest (specificity ≥97%). Receiver operating characteristic curves visualized all combinations of sensitivity and specificity of decision trees within this range. Results showed that decision trees are easy to interpret when visualised. The lower the confidence factor, the smaller the decision trees: a cost insensitive decision tree with a confidence factor of 0.05 needed only eleven test nodes to classify all 3097 records with a sensitivity of 23.7% and a specificity of 99.2%. The decision tree with default parameter settings showed a transformed partial area under the curve value of 0.6420. By introducing costs for false negative classifications this value increased to 0.6476. At a specificity level of 99%, the decision tree with the highest transformed partial area under the curve value showed a sensitivity of 29.8%. Detection performances of the different decision trees were comparable with those of models currently used by automatic milking systems. As it was possible to achieve these results with the use of a rather simple decision tree algorithm, we believe that decision tree induction shows potential for detecting clinical mastitis with automatic milking.

An automated in-line clinical mastitis detection system using measurement of conductivity from foremilk of individual udder quarters

AIM: To assess a novel method for automatic in-line detection of clinical mastitis. METHODS: For a brief period at the start of milking for each cow, electrical conductivity of foremilk was measured for each quarter in turn, using a single sensor installed in the long milk tube (LMT) about 1.5 m downstream from the milking-machine claw. Sequential separation of flow between udder quarters was achieved by control of pulsation to individual teatcups within a conventional cluster. The ratio of conductivity values between quarters was used as an indicator of mastitis status. The concept was evaluated initially in a pilot trial in a 200-cow herd milked in a 23-stall swing-over herringbone milking parlour. It was then tested rigorously in a field trial in a 640-cow herd milked in a 50-stall rotary milking parlour. Both trials were conducted in the Waikato region of New Zealand. In the latter trial, sensor results were compared with visual inspection of a commercial in-line mastitis filter fitted to each milking unit. These filters were inspected for clots immediately after every cow's milking, for 3 weeks. The dataset of approximately 27,000 individual milkings was tested against several published or potential alter- native ‘gold standards’ for diagnosing clinical mastitis. RESULTS: In the pilot trial, 12–14 clinical events were detected out of 19 true clinical quarters, with a false-alert rate of between three and five false electrical-conductivity alerts per 1,000 individual milkings. In the more rigorous field trial, sensitivity ranged from 68 to 88%, and the false-alert rate (false-alert episodes per 1,000 individual milkings) ranged from 2.3 to 7.0. CONCLUSION: The novel clinical mastitis detection system, based on separation of the flow and measurement of electrical conductivity from foremilk of individual udder quarters, has the potential to provide a new tool for helping farmers to monitor clinical mastitis in herds milked with conventional clusters.

Detection of Clinical Mastitis with the Help of a Thermal Camera

Increasing dairy farm size and increase in automation in livestock production require that new methods are used to monitor animal health. In this study, a thermal camera was tested for its capacity to detect clinical mastitis. Mastitis was experimentally induced in 6 cows with 10μg of Escherichia coli lipopolysaccharide (LPS). The LPS was infused into the left forequarter of each cow, and the right forequarters served as controls. Clinical examination for systemic and local signs and sampling for indicators of inflammation in milk were carried out before morning and evening milking throughout the 5-d experimental period and more frequently on the challenge day. Thermal images of experimental and control quarters were taken at each sampling time from lateral and medial angles. The first signs of clinical mastitis were noted in all cows 2h postchallenge and included changes in general appearance of the cows and local clinical signs in the affected udder quarter. Rectal temperature, milk somatic cell count, and electrical conductivity were increased 4h postchallenge and milk N-acetyl-β-D-glucosaminidase activity 8h postchallenge. The thermal camera was successful in detecting the 1 to 1.5°C temperature change on udder skin associated with clinical mastitis in all cows because temperature of the udder skin of the experimental and control quarters increased in line with the rectal temperature. Yet, local signs on the udder were seen before the rise in udder skin and body temperature. The udder represents a sensitive site for detection of any febrile disease using a noninvasive method. A thermal camera mounted in a milking or feeding parlor could detect temperature changes associated with clinical mastitis or other diseases in a dairy herd.

Automatic Detection of Clinical Mastitis Is Improved by In-Line Monitoring of Somatic Cell Count

This study explored the potential value of in-line composite somatic cell count (ISCC) sensing as a sole criterion or in combination with quarter-based electrical conductivity (EC) of milk, for automatic detection of clinical mastitis (CM) during automatic milking. Data generated from a New Zealand research herd of about 200 cows milked by 2 automatic milking systems during the 2006-2007 milking season included EC, ISCC, monthly laboratory-determined SCC, and observed cases of CM that were treated with antibiotics. Milk samples for ISCC and laboratory-determined SCC were taken sequentially at the end of a cow milking. Both samples were derived from a composite cow milking obtained from the bottom of the milk receiver. Different time windows were defined in which true-positive, false-negative, and false-positive alerts were determined. Quarters suspected of having CM were visually checked and, if CM was confirmed, sampled for bacteriological culturing and treated with an antibiotic treatment. These treated quarters were considered as gold-standard positives for comparing CM detection models. Alert thresholds were adjusted to achieve a sensitivity of 80% in 3 detection models: using ISCC alone, EC alone, or a combination of these. The success rate (also known as the positive predictive value) and the false alert rate (number of false-positive alerts per 1,000 cow milkings) were used to evaluate detection performance. Normalized ISCC estimates were highly correlated with normalized laboratory-determined SCC measurements (r=0.82) for SCC measurements >200×103 cells/mL. Using EC alone as a detection tool resulted in a range of 6.9 to 11.0% for success rate, and a range of 4.7 to 7.8 for the false alert rate. Values for the ISCC model were better than the model using EC alone with 12.7 to 15.6% for the success rate and 2.9 to 3.7 for the false alert rate. Combining sensor information to detect CM, by using a fuzzy logic algorithm, produced a 2- to 3-fold increase in the success rate (range 21.9 to 32.0%) and a 2- to 3-fold decrease in the false alert rate (range 1.2 to 2.1) compared with the models using ISCC or EC alone. Results suggest that the performance of a CM detection system improved when ISCC information was added to a detection model using EC information.

Evaluation of a novel chemical sensor system to detect clinical mastitis in bovine milk

Automatic detection of clinical mastitis is an essential part of high performance and robotic milking. Currently available technology (conductivity monitoring) is unable to achieve acceptable specificity or sensitivity of detection of clinical mastitis or other clinical diseases. Arrays of sensors with high cross-sensitivity have been successfully applied for recognition and quantitative analysis of other multicomponent liquids. An experiment was conducted to determine whether a multisensor system (“electronic tongue”) based on an array of chemical sensors and suitable data processing could be used to discriminate between milk secretions from infected and healthy glands. Measurements were made with a multisensor system of milk samples from two different farms in two experiments. A total of 67 samples of milk from both mastitic and healthy glands were in two sets. It was demonstrated that the multisensor system could distinguish between control and clinically mastitic milk samples ( p = 0.05). The sensitivity and specificity of the sensor system (93 and 96% correspondingly) showed an improvement over conductivity (56 and 82% correspondingly). The multisensor system offers a novel method of improving mastitis detection.

Efeito da suplementação de vitamina A sobre a incidência de mastite em vacas da raça Holandesa

This experiment was designed to evaluate the influence of prepartum vitamin A supplementation on incidence of mastitis in Holstein cows. Forty two cows were randomly distributed into blocks according to the parity and divided in two treatments: control and supplemented with 180,000IU vit.A/day. The supplementation started 30 days before probable parturition date up to parturition. The Tamis test was performed weekly for clinical mastitis detection, and milk samples were collected for somatic cell counts. Vitamin A treatment decreased the incidence of clinical mastitis (P<0.05), however no effect on milk somatic cell count was observed.

The Effect of Selective Dry Cow Treatment on New Intramammary Infections

Dry cow therapy, or antibiotic treatment at end of lactation, is used to eliminate intramammary infections and prevent new infections during the dry period. It is one part of a total management system recommended in controlling intramammary infections in the dairy cow. Public health concerns advise prudent use of antibiotics, as their use may promote bacterial antibiotic resistance and leave antibiotic residues in the food chain. The effects of dry cow treatment and no treatment were compared, on new intramammary infections and clinical mastitis within two low cell count herds and two herds undergoing conversion to organic farming. The results will inform those restricting their use of dry cow therapy on the additional risk of new intramammary infection and aid in development of alternative management strategies. No cases of clinical mastitis in the dry period were observed in treated cows, whereas in the untreated groups a significant number were observed. Significantly more new infections at calving were found in the untreated group in all herds. In those quarters where infections were first detected at calving, the incidence of clinical mastitis was significantly greater in the untreated group in all herds. Clinical mastitis detection was significantly lower in organic herds. Untreated quarters infected at drying with Corynebacterium spp. or coagulase-negative staphylococci were found to have an increased risk of new infection by Streptococcus uberis or coliform bacteria. It can be concluded that dry cow therapy continues to lower significantly the rate of new dry period intramammary infection in herds with elevated somatic cell counts and a high prevalence of infection.

Detection of Clinical Mastitis by Changes in Electrical Conductivity of Foremilk Before Visible Changes in Milk

Mastitis was induced by the direct infusion of Staphylococcus aureus or Streptococcus uberis into the mammary gland of lactating cows. Changes in electrical conductivity of foremilk indicated the establishment of bacteria, increased SCC, increased clotting of milk, and, hence, disease in advance of visible changes in the milk that could be diagnosed by a herdsperson. Clinical mastitis was detectable by changes in electrical conductivity of foremilk, 90% of cases were detectable when clots first appeared in foremilk, and 55% of cases were detectable up to 2 milkings prior to the appearance of clots. All subclinical infections from Staph. aureus were detected, but subclinical infections from Strep. uberis were not detected. The result suggested that clinical mastitis caused by these two major pathogens could e detected earlier by measuring changes in electrical conductivity of milk than by waiting for a herdsperson to detect visible changes in milk. Earlier detection would permit earlier treatment. However, the hand-held sensor used in this experiment is impractical for commercial application, and reliable automated sensors and decision-making algorithms are required.

Comparison of Analysis Techniques for On-Line Detection of Clinical Mastitis

Three techniques were compared for analysis of automatically collected data from the milking parlor. Mammary quarters showing signs of clinical mastitis were compared with randomly selected healthy quarters. Automatic data were analyzed from the milking on which the milkers observed clinical mastitis as well as data from the two prior milkings. Electrical conductivity of milk was not corrected for individual cows. Milking parlor data were preprocessed so that information on the electrical conductivity pattern during a milking was retained.Principal component analysis was used to verify whether variation in the data was caused by mastitis. Performance of logistic regression models for detection of clinical mastitis was compared with that of backpropagation neural networks.Variation in the quarter data was caused by mastitis. Automatic data from infected quarters did not always differ from data from healthy quarters, especially from the two prior milkings. The detection performance of the logistic regression model was similar to that of the neural networks. When both models were tested on the development data, sensitivity was approximately 75%, and specificity was approximately 90% at the milking of mastitis observation. Detection results were lower for the prior milkings. Therefore, not all incidences of clinical mastitis cases could be detected before clinical signs occurred.

Application of a neural network to analyse on-line milking parlour data for the detection of clinical mastitis in dairy cows

As part of research towards an on-line mastitis detection system, standard back-propagation neural networks to classify healthy and clinical mastitic quarters were explored. The usage of back-propagation neural networks is discussed. A neural network for clinical mastitis detection is presented. This network used automatically collected quarter electrical conductivity data as input. The network was trained with 17 healthy and 13 clinical mastitic quarters. All healthy and 12 of 13 mastitic quarters were classified correctly after training. The trained neural network predicted 34 of 38 healthy quarters correctly in different evaluation data sets. For mastitic quarters, related data had to be used, and 21 of 38 mastitic quarters were classified correctly. We concluded that a back-propagation neural network could indeed separate healthy from clinical quarters in an experimental setting. Further development should include the use of different input parameters and comparisons with other analysis techniques.

Electrical Conductivity of Milk: Measurement, Modifiers, and Meta Analysis of Mastitis Detection Performance

The physics, physiology, and pathology of electrical conductivity of milk are described. Based on a meta analysis, the use of electrical conductivity as a mastitis detection tool is discussed. Most reports were based on subclinical mastitis data. The gold standards of the different reports are discussed. With an overall sensitivity of 66% and an overall specificity of 94%, the predictive value of a positive electrical conductivity test remains low in a low prevalence population. The use of on-line systems for clinical mastitis detection is discussed. Online systems that combine multiple data and perform multifactorial analyses will be of interest to the dairy industry.

A diagnostic approach to educating Minnesota dairy farmers in the prevention and control of bovine mastitis

Six characteristics of innovations described as influencing their rates of adoption by farmers are their relative advantage, complexity, ability to be tried, visibility of outcome personal contact between the advocate and the farmer, and compatibility with existing values. In this study, these are related to the adoption of mastitis prevention and control procedures. An educational diagnostic method described by Green et al. was adapted from human health education and applied to mastitis prevention and control with a group of farmers form nine Minnesota dairy farms. The method included six phases. A social diagnosis determined farmers' perceived rewards from dairying to be profit-making, enjoyment of working with animals, a desire to see cows achieve their production potential and a desire to raise a family in a farm setting. An epidemiological diagnosis from the literature determined the reduction of new infection rate and increase in the infection elimination rate to be the principal means of reducing mastitis prevalence. Teat dipping, dry period therapy, detection of clinical mastitis, segregating infected cows at milking, correct udder preparation and correct function and use of the milking machine were the main behavioral factors involved in mastitis control. An educational diagnosis identified pre-disposing, enabling and reinforcing factors associated with mastitis control. Their use in a mastitis control education program is illustrated using the teat dipping behavior as an example. Planning implementation was the fifth phase of the method. It involved rating the importance to mastitis control of various possible control behaviors and the likelihood of changing them. A priority order for employing resources was established from these. Planning also involved defining the behavior changes desired of farmers in mastitis control. The final phase was evaluation. Information from a survey of 222 Minnesota dairy farms was also incorporated in the process. The diagnostic method proved valuable in helping the educators view mastitis from the farmers point of view. A practical educational program was produced.

The detection of clinical mastitis with in-line filters

SummaryA simple in-line filter is described which can be fitted into the milk tube of a. milking machine in order to detect clinical mastitis. It consists of a stainless steel mesh, on which the clots are retained, and it is fitted obliquely into a short length of clear plastics tube. The filter has been developed as an alternative to the fore-milk cup. Tests made over 9 months in a 60-cow herd show good agreement between the fore-milk cup and the in-line filter recordings. For general service production the filters are made by moulding.