Deep learning classification method for boar sperm morphology analysis.

ABSTRACT:

Background

Boar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics.

Objective

To overcome these challenges, we propose a novel approach integrating deep-learning technology with high-throughput image-based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label-free acrosome health of thousands of individual spermatozoa at once, a

SUBMITTER: Keller A 

PROVIDER: S-EPMC12368927 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature