Development and evaluation of a standardized technique to assess teat skin temperature of dairy cows using infrared thermography.

Much research has been done to develop methods to assess dimensional teat traits in dairy cows. In contrast, diagnostic techniques to reliably assess the circulatory system of teats are limited. Infrared thermography facilitates measurements of skin temperature and could be used to detect physiological and pathological changes to the teat tissue associated with machine milking, as temperature reflects the underlying blood circulation and tissue metabolism. Our objective was to develop and evaluate a scanning technique to quantify teat skin temperature in dairy cows using infrared thermography. Using a portable thermography camera, 2 operators obtained duplicate scans of both hind teats from 20 Holstein cows, resulting in 80 thermographic images (20 cows × 2 operators × 2 images). Average teat skin temperatures at the proximal, middle, and distal teat aspects were determined. We used Pearson correlation coefficients (r), intraclass correlation coefficients (ICC), and concordance correlation coefficients (CCC) to assess interoperator reproducibility (i.e., agreement between measurements performed by different operators) and intraoperator repeatability (i.e., agreement between measurements performed by the same operator). Pearson correlation coefficients revealed a very strong correlation for measurements at the proximal, middle, and distal aspects of the teat, respectively, between operators (r ≥0.95) and duplicate scans (r ≥0.94) within operators. Intraclass correlation coefficients and CCC indicated excellent interoperator reproducibility (ICC ≥0.95, CCC ≥0.95) and excellent intraoperator repeatability (ICC ≥0.94, CCC ≥0.94), respectively, for measurements at all 3 aspects. Least squares means (95% confidence interval) for average teat skin temperatures at the proximal, middle, and distal teat aspects, respectively, were 33.2 (32.6-33.8), 32.4 (31.5-33.2), and 30.9 (29.8-32.0) °C for operator 1, and 33.2 (32.6-33.8), 32.4 (31.6-33.3), and 31.0 (29.9-32.0) °C for operator 2. Average temperatures between duplicate scans within operators at the proximal, middle, and distal aspects, respectively, were 33.3 (32.7-33.9), 32.5 (31.7-33.3), and 31.0 (29.9-32.1) °C for the first scan and 33.2 (32.6-33.8), 32.3 (31.5-33.1), and 30.8 (29.7-31.9) °C for the second scan. We conclude that infrared thermography facilitates precise measurements of skin temperatures of cows' hind teats.

Delta thermal radiomics: An application in dairy cow teats.

We describe a novel approach for analyzing thermal images by way of radiomics (i.e., thermal radiomics) and how it can be used to monitor short-term temperature changes of dairy cow hind teats; that is, delta thermal radiomics. The heat generated from metabolic activities and blood-flow patterns can be visualized using thermal radiography of the skin surface. The hind teats from 25 dairy cows were imaged with a digital thermal camera and the images were converted to medical images (DICOM format) by mapping the multi-channel colorized thermal image to a monochromatic image whose intensities represent temperature. The 50 teats (left and right hind) were then manually segmented by 2 investigators. Radiomics analysis, which is a common method of extracting semantic and nonsemantic image biomarkers from medical images for machine learning, was performed. To evaluate whether this approach can detect pre- and postmilking differences, 18 cows were imaged before and after milking, the teats were manually segmented, and radiomic calculations were performed. Student's t-test was used to provide an estimate of the likelihood of whether postmilking thermal image biomarkers are the same as premilking thermal image biomarkers, and Cohen's d was used to evaluate the size of the effect (d > 1.2). To evaluate uncertainties from manual segmentation, the Dice similarity score (DS) between the 2 investigators' segments was computed. The average DS (95% confidence limit) was 0.952 (0.913-0.982) when comparing the 2 investigators' segmentations. There was no significant difference in DS when comparing the left and right segmented teats, suggesting that teats can be segmented consistently. No differences (d < 0.36) were observed when comparing image biomarkers from one investigator's segments with the other's, suggesting that image biomarkers computed from one investigator's segmentation of teats are not likely to differ from those computed from the other investigator. When comparing image biomarkers before and after milking, 109 image biomarkers were analyzed, and 17 image biomarkers were simultaneously significant and exhibited effect size. Thus, delta thermal radiomics offers a noninvasive and quantitative method of monitoring skin temperature changes in humans and animals after an intervention. The advantage of this approach is that it can reveal both perceptible and imperceptible surface temperature features that may be useful for detecting and managing dairy teat health.

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

Infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows; therefore, sustaining the integrity of the teat canal and its adjacent tissues is critical to resist infection. The ability to monitor teat tissue condition is a key prerequisite for udder health management in dairy cows. However, to date, routine assessment of teat condition is limited to cow-side visual inspection, making the evaluation a time-consuming and expensive process. Here, we demonstrate a digital teat-end condition assessment by way of deep learning. A total of 398 digital images from dairy cows' udders were collected on 2 commercial farms using a digital camera. The degree of teat-end hyperkeratosis was scored using a 4-point scale. A deep learning network from a transfer learning approach (GoogLeNet; Google Inc., Mountain View, CA) was developed to predict the teat-end condition from the digital images. Teat-end images were split into training (70%) and validation (15%) data sets to develop the network, and then evaluated on the remaining test (15%) data set. The areas under the receiver operator characteristic curves on the test data set for classification scores of normal, smooth, rough, and very rough were 0.778 (0.716-0.833), 0.542 (0.459-0.608), 0.863 (0.788-0.906), and 0.920 (0.803-0.986), respectively. We found that image-based teat-end scoring by way of deep learning is possible and, coupled with improvements in image acquisition and processing, this method can be used to assess teat-end condition in a systematic and efficient manner.

Technical note: A digital technique and platform for assessing dairy cow teat-end condition.

Because infections with pathogenic bacteria entering the mammary gland through the teat canal are the most common cause of mastitis in dairy cows, sustaining the integrity of the teat canal and its adjacent tissues is critical to resist infection. The ability to monitor teat tissue condition is therefore a key prerequisite for udder health management in dairy cows. However, to date, routine assessment of teat-end condition is limited to cow-side visual inspection, making the evaluation a time-consuming and expensive process. Here, we illustrate and demonstrate a method for assessing teat-end condition of dairy cows through digital images and software. A digital workflow has been designed where images of dairy cow teats are obtained and processed to display individual teats, and the cow and teat images are labeled and displayed through a graphical user interface. The interface then allows an evaluator to assess quarter- and cow-level teat-end condition and store the results for review and future analysis. The digital workflow permits several advantages such as the ability to perform remote teat-end condition assessments, and assess inter- and intrarater variability of teat-end condition scoring. We demonstrate the image-based teat-end condition assessment of 194 dairy cows that also had cow-side teat-end condition assessments by 2 expert evaluators. Weighted Cohen's kappa statistic (κ) was computed to measure the evaluators' concordance of categorical scores of quarter- and cow-level assessments when using cow-side and image-based assessments. Substantial agreement (0.61 ≤ κ ≤ 0.80) was observed between an evaluator's cow-side and image-based assessments at the quarter and cow level. Moderate agreement (0.41 ≤ κ ≤ 0.60) was observed between evaluators when using image-based assessments at the quarter and cow level. Near perfect agreement (κ = 0.89, 95% confidence interval 0.78-1.00) was observed between evaluators when using cow-side assessments at the quarter level, and substantial agreement (κ = 0.66, 95% confidence interval 0.53-0.79) was observed when using cow-side assessments at the cow level. This suggests that image-based teat-end condition classification is possible, and coupled with improvements in image acquisition and image processing, this method can be used to assess teat-end condition in a systematic and convenient manner.

Blood perfusion of teat tissue in dairy cows: Changes associated with pre-milking stimulation and machine milking.

Mechanical forces during machine milking of dairy cows evoke circulatory impairment of the teat tissue that may affect the teats' defense mechanisms against mastitis pathogens. Ample research describes dimensional changes of different teat traits after machine milking, whereas reports that describe changes in blood circulation of dairy cows' teats are limited. Therefore, the objectives of this study were to (1) describe changes in teat blood circulation that occur after pre-milking teat stimulation and machine milking and (2) study the effect of 2 different milking liners on machine milking-induced changes in teat blood flow. In a randomized trial, Holstein dairy cows were stratified by parity, stage of lactation, and average daily milk yield during the previous week, and allocated to 1 of 2 treatment groups. Treatment consisted of 1 milking observation with either a round or multisided concave milking liner. Teat scans were taken of the left front and the right hind teats using power Doppler ultrasonography. Imaging occurred before pre-milking udder preparation (T1), after completion of pre-milking udder preparation but before milking-unit attachment (T2), and immediately after unit detachment (T3). Perfusion intensity measurements from teat scans were performed with a commercially available software program. Data from 109 cows were analyzed. A general linear mixed model showed differences in perfusion intensity between time points. Least squares means (95% confidence intervals) for T1, T2, and T3, respectively, were 0.035% (0.026-0.047), 0.124% (0.093-0.164), and 0.095% (0.073-0.124). Conversely, no statistically significant differences between treatment groups were observed. We conclude that teat blood circulation is subjected to several influences, including inherent circulatory regulation mechanisms, as well as extrinsic factors such as machine milking. Future research is warranted to decipher the magnitude of their influence and to further our understanding of how these changes relate to the susceptibility to intramammary infection and milking performance.

Technical note: Development and evaluation of a standardized technique to assess blood perfusion in teats of dairy cows using power Doppler ultrasonography.

Ample research has described the assessment of dimensional changes for different teat traits, whereas diagnostic techniques to reliably assess blood circulation in teats of dairy cows are limited. Here, we describe the development and evaluation of a scanning technique to quantify blood flow in teats of dairy cows using power Doppler ultrasonography. In 2 consecutive trials, 384 teat scans [trial 1, n = 256 (sagittal plane, n = 128; transverse plane, n = 128); trial 2, n = 128 (transverse plane)] from 16 cows were obtained by the same 2 operators. Perfusion intensity from single images (trial 1) and video images (trial 2) were assessed using a commercially available software program. Intraclass correlation coefficients (ICC) and concordance correlation coefficients (CCC) were used to assess interoperator reproducibility (agreement between measurements performed by different operators) and intraoperator repeatability (agreement between measurements performed by the same operator). In trial 1, interoperator ICC and CCC indicated poor agreement (ICC ≤0.26, CCC ≤0.26). Intraoperator ICC and CCC demonstrated poor agreement between duplicate measurements within operators (ICC ≤0.19, CCC ≤0.19). Modifications after trial 1 included (1) a different ultrasound device, (2) analysis of video clips rather than single images, (3) restriction to 1 sectional plane (i.e., transverse), and (4) a scanning sequence such that repeated scans within operators were measured one after another. Through these modifications, intraoperator repeatability in trial 2 yielded fair to good agreement, with intraoperator ICC and CCC over both operators ranging from 0.44 to 0.70 and from 0.57 to 0.69, respectively, whereas interoperator ICC and CCC showed poor agreement (ICC = 0.35, CCC = 0.34). We conclude that repeatable measurements of blood perfusion intensity of teats in dairy cows can be attained with power Doppler ultrasonography. Power Doppler ultrasonography is a suitable tool to quantify slow flow in small vessels and may be an acceptable diagnostic technique to assess changes in blood circulation that result from machine milking in teats of dairy cows, although further research is necessary to validate this hypothesis.