Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Utrecht, The Netherlands.
Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada.
PLoS One. 2023 Jan 17;18(1):e0280098. doi: 10.1371/journal.pone.0280098. eCollection 2023.
Low-effort, reliable diagnostics of digital dermatitis (DD) are needed, especially for lesions warranting treatment, regardless of milking system or hygienic condition of the feet. The primary aim of this study was to test the association of infrared thermography (IRT) from unwashed hind feet with painful M2 lesions under farm conditions, with lesion detection as ultimate goal. Secondary objectives were to determine the association between IRT from washed feet and M2 lesions, and between IRT from unwashed and washed feet and the presence of any DD lesion. A total of 641 hind feet were given an M-score and IRT images of the plantar pastern were captured. Multivariable logistic regression analyses were done with DD status as dependent variable and maximum infrared temperature (IRTmax), lower leg cleanliness score and locomotion score as independent variables, and farm as fixed effect. To further our understanding of IRTmax within DD status, we divided IRTmax into two groups over the median value of IRTmax in the datasets of unwashed and washed feet, respectively, and repeated the multivariable logistic regression analyses. Higher IRTmax from unwashed hind feet were associated with M2 lesions or DD lesions, in comparison with feet without an M2 lesion or without DD, adjusted odds ratio 1.6 (95% CI 1.2-2.2) and 1.1 (95% CI 1.1-1.2), respectively. Washing of the feet resulted in similar associations. Dichotomization of IRTmax substantially enlarged the 95% CI for the association with feet with M2 lesions indicating that the association becomes less reliable. This makes it unlikely that IRTmax alone can be used for automated detection of feet with an M2 lesion. However, IRTmax can have a role in identifying feet at-risk for compromised foot health that need further examination, and could therefore function as a tool aiding in the automated monitoring of foot health on dairy herds.
需要一种低投入、可靠的数字皮炎（DD）诊断方法，特别是对于需要治疗的病变，无论其挤奶系统或脚部的卫生条件如何。本研究的主要目的是测试未清洗后脚的红外热成像（IRT）与农场条件下疼痛的 M2 病变之间的关联，以检测病变为最终目标。次要目标是确定清洗后脚的 IRT 与 M2 病变之间的关联，以及未清洗和清洗后脚的 IRT 与任何 DD 病变之间的关联。共对 641 只后脚进行了 M 评分，并拍摄了足底跗关节的 IRT 图像。将 DD 状态作为因变量，最大红外温度（IRTmax）、小腿清洁度评分和运动评分作为自变量，农场作为固定效应，进行多变量逻辑回归分析。为了进一步了解 IRTmax 在 DD 状态下的情况，我们将 IRTmax 分为两组，分别为未清洗和清洗后脚数据集的 IRTmax 中位数以上和以下，并重复多变量逻辑回归分析。与没有 M2 病变或没有 DD 的脚相比，未清洗后脚的 IRTmax 较高与 M2 病变或 DD 病变相关，调整后的优势比分别为 1.6（95%CI 1.2-2.2）和 1.1（95%CI 1.1-1.2）。洗脚也会产生类似的关联。IRTmax 的二分法大大扩大了与 M2 病变脚相关的 95%CI，表明这种关联变得不太可靠。这使得 IRTmax 本身不太可能用于自动检测 M2 病变的脚。然而，IRTmax 可以用于识别脚部健康受损的风险，需要进一步检查，因此可以作为一种工具，辅助对奶牛群的脚部健康进行自动监测。
