Diagnostic Screening of Bovine Mastitis Using MALDI-TOF MS Direct-Spotting of Milk and Machine Learning

Abstract

Novel strategies for diagnostic screening of animal and herd health are crucial to contain disease outbreaks, maintain animal health, and maximize production efficiency. Mastitis is an inflammation of the mammary gland in dairy cows, often resulting from infection from a microorganism. Mastitis outbreaks result in loss of production, degradation of milk quality, and the need to isolate and treat affected animals. In this work, we evaluate MALDI-TOF mass spectrometry as a diagnostic for the culture-less screening of mastitis state from raw milk samples collected from regional dairies. Since sample preparation requires only minutes per sample using microvolumes of reagents and no cell culture, the technique is promising for rapid sample turnaround and low-cost diagnosis. Machine learning algorithms have been used to detect patterns embedded within MALDI-TOF spectra using a training set of 226 raw milk samples. A separate scoring set of 100 raw milk samples has been used to assess the specificity (spc) and sensitivity (sens) of the approach. Of machine learning models tested, the gradient-boosted tree model gave global optimal results, with the Youden index of J = 0.7, sens = 0.89, and spc = 0.81 achieved for the given set of conditions. Random forest models also performed well, achieving J > 0.63, with sens = 0.83 and spc = 0.81. Naïve Bayes, generalized linear, fast large-margin, and deep learning models failed to produce diagnostic results that were as favorable. We conclude that MALDI-TOF MS combined with machine learning is an alternative diagnostic tool for detection of high somatic cell count (SCC) and subclinical mastitis in dairy herds.

A New Method to Detect Buffalo Mastitis Using Udder Ultrasonography Based on Deep Learning Network

“…However, using this detection technology to determine udder health is susceptible to external factors, leading to inaccurate detection of mastitis [ 18 ]. Furthermore, a mastitis detection network has been established based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry [ 29 ], sensors [ 30 ], cytometric fingerprinting [ 31 ], and other technologies. In several studies, deep learning-based mastitis detection exhibited an accuracy of above 80% [ 16 , 32 ], but the equipment or big data required for mastitis detection are highly likely to be unavailable to buffalo farms.…”

A Systematic Review of the Recent Techniques Commonly Used in the Diagnosis of Mycoplasma bovis in Dairy Cattle

“…In the most recent attempt, Thompson et al (2023) combined MALDI-TOF with machine learning as an alternative diagnostic tool to detect the high somatic cell count (SCC) and subclinical mastitis in dairy herds around Texas, USA. Their study involving 100 milk samples showed a high sensitivity and specificity [ 38 ]. As much as the technique is reliable, rapid, and cost-effective for routine identification of unknown Mycoplasma isolates, it suffers from the limitations of the culture technique.…”

Advances in Diagnostic Approaches and Therapeutic Management in Bovine Mastitis

“…With an extended algae database, MALDI-TOF MS can contribute to the speciation of Prototheca from mastitis cases quickly and easily. Many studies [ 214 , 215 , 216 , 217 , 218 , 219 ] evaluated the use of MALDI-TOF MS as an alternative method for the large-scale identification of conventional/non-conventional/antimicrobial-resistant bacteria isolates from milk samples of dairy animals compared with classical microbiological routine protocols with greater specificity and sensitivity.…”