Machine Learning Approach for Muscovy Duck (Cairina moschata) Semen Quality Assessment

This study aimed to develop a comprehensive approach for assessing fresh ejaculate from Muscovy duck (Cairina moschata) drakes to fulfil the requirements of artificial insemination in farm practices. The approach combines sperm kinetics (CASA) with non-kinetic parameters, such as vitality, enzyme activities (alkaline phosphatase (AP), creatine kinase (CK), lactate dehydrogenase (LDH), and gamma-glutamyl-transferase (GGT)), and total DNA methylation as training features for a set of machine learning (ML) models designed to enhance the predictive capacity of sperm parameters. Samples were classified based on their progressive motility and DNA methylation features, exhibiting significant differences in total and progressive motility, curvilinear velocity (VCL), velocity of the average path (VAP), linear velocity (VSL), amplitude of lateral head displacement (ALH), beatcross frequency (BCF), and live normal sperm cells in favour of fast motility ones. Additionally, there were significant differences in enzyme activities for AP and CK, with correlations to LDH and GGT levels. Although motility showed no correlation with total DNA methylation, ALH, wobble of the curvilinear trajectory (WOB), and VCL were significantly different in the newly introduced classification for suggested good quality, where both motility and methylation were high. The performance differences observed while training various ML classifiers using different feature subsets highlight the importance of DNA methylation for achieving more accurate sample quality classification, even though there is no correlation between motility and DNA methylation. The parameters ALH, VCL, triton extracted LDH, and VAP were top-ranking for suggested good quality predictions by the neural network and gradient boosting models. In conclusion, integrating nonkinetic parameters into machine-learning-based sample classification offers a promising approach for selecting kinetically and morphologically superior duck sperm samples that might otherwise be hindered by a predominance of lowly methylated cells.

Automated summary

This study aimed to develop a comprehensive approach using machine learning models to assess fresh ejaculate from Muscovy duck drakes, combining sperm kinetics with non-kinetic parameters such as vitality, enzyme activities, and total DNA methylation. The samples showed significant differences in motility, curvilinear velocity, linear velocity, amplitude of lateral head displacement, beatcross frequency, and live normal sperm cells between different classifications based on progressive motility and DNA methylation features. Integrating nonkinetic parameters into machine-learning-based sample classification offers a promising approach for selecting high-quality duck sperm samples.