Abstract
Sperm morphology assessment is recognised as a critical, yet variable, test of male fertility. This variability is due in part to the lack of standardised training for morphologists. This study utilised a bespoke 'Sperm Morphology Assessment Standardisation Training Tool' to train novice morphologists using machine learning principles and consisted of two experiments. Experiment 1 assessed novice morphologists' (n = 22) accuracy across 2- category (normal; abnormal), 5- category (normal; head defect, midpiece defect, tail defect, cytoplasmic droplet), 8- category (normal; cytoplasmic droplet; midpiece defect; loose heads and abnormal tails; pyriform head; knobbed acrosomes; vacuoles and teratoids; swollen acrosomes), and 25- category (normal; all defects defined individually) classification systems, with untrained users achieving 81.0 ± 2.5%, 68 ± 3.59%, 64 ± 3.5%, and 53 ± 3.69%, respectively. A second cohort (n = 16) exposed to a visual aid and video significantly improved first-test accuracy (94.9 ± 0.66%, 92.9 ± 0.81%, 90 ± 0.91% and 82.7 ± 1.05, p < 0.001). Experiment 2 evaluated repeated training over four weeks, resulting in significant improvement in accuracy (82 ± 1.05% to 90 ± 1.38%, p < 0.001) and diagnostic speed (7.0 ± 0.4s to 4.9 ± 0.3s, p < 0.001). Final accuracy rates reached 98 ± 0.43%, 97 ± 0.58%, 96 ± 0.81%, and 90 ± 1.38% across classification systems 2-, 5-, 8- and 25-categories respectively. Significant differences in accuracy and variation were observed between the classification systems. This tool effectively standardised sperm morphology assessment. Future research could explore its application in other species, including in human andrology, given its accessibility and adaptability across classification systems.