Diagnostic and economic evaluation of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with machine learning for screening of Johne's disease from dairy cow serum
Year: 2025
Type: article
Abstract: Johne's disease (JD) is an infectious bacterial disease (Mycobacterium avium ssp. paratuberculosis) that primarily affects the intestinal tract and associated lymph nodes of ruminants, especially in cattle. It is both a herd-level problem and an individual animal problem, as much of the infection is subclinical. This disease has a significant impact due to drastic production losses and early culling of animals. Here, we used MALDI-TOF MS for diagnostic screening of JD state from serum samples collected in the panhandle of Texas. We used several machine learning algorithms for observing patterns embedded in MALDI-TOF spectra using a training set of 37 serum samples. A separate set of scoring and evaluation samples (n = 25) from different animals was used to evaluate the accuracy of each machine learning model and assess the sensitivity (sens) and specificity (spc). Next, a partial budget analysis was used to calculate the net present value of using a MALDI-TOF-based test in a testing and culling strategy relative to existing tests and culling techniques. The random forest model yielded predictive results of optimal sens = 0.80, spc = 0.83, and Youden index (J) above 0.63. The partial budget analysis found that using a MALDI + PCR scenario was the most cost-effective strategy, reducing the net present value of JD losses by $13.11 per head compared with the no-control scenario. We conclude that MALDI-TOF MS of serum samples with machine learning provides a moderately effective diagnostic tool for JD diagnosis in cattle.
Source: Journal of Dairy Science
Authors: Sumon Sarkar, Bailey A. Young, C. Scanlon Daniels, Jonathan E. Thompson
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,500
OpenAlex ID: W4415314242