Machine Learning Approach for Early Lactation Mastitis Diagnosis Using Total and Differential Somatic Cell Counts

Abstract

Dairy herds around the world are undergoing several changes. Herd sizes are increasing, as are both milk yield and quality. The implementation of new technologies in various domains of dairy production is leading to an increase in the quantity of data available. This, in turn, creates a need to extract useful information from these data to improve production efficiency. This paper presents the findings of a preliminary study that utilizes a machine learning (ML) approach to assess the accuracy of somatic cell count (SCC) and neutrophils + lymphocytes count/mL (PLCC) in identifying cows at risk of developing intramammary infection (IMI) due to major pathogens. These pathogens (MajPs) include S. aureus, S. agalactiae, S. uberis, and S. dysgalactiae. This study identified these pathogens either by real-time PCR (qPCR) methods or by conventional bacteriology, following the cows’ calving process. This study encompassed a total of 424 cows and 1696 quarter milk samples. A comparison of the two methods revealed significant disparities in the prevalence of MajPs, with the qPCR method demonstrating a higher prevalence than conventional bacteriology. However, the prevalence of negative results was comparable, with both methods yielding approximately 71.0% and 72.1%, respectively. The comprehensive results of this study substantiated that all the cellular markers exhibited the most accurate when MajP IMI was diagnosed using quarter milk samples, but this result is mainly due to the very high specificity. The cellular markers exhibited nearly equivalent performance, irrespective of the ML algorithm employed. The findings indicate that approaches based on SCC or PLCC may be useful for identifying healthy cows or quarters. However, it is essential to confirm all “non-negative” results through subsequent analysis within 7–15 days to ensure accuracy. However, further studies are necessary to enhance diagnostic accuracy.

Keywords

• mastitis
• antimicrobial resistance
• early diagnosis
• differential cell count
• machine learning