AI-Assisted Sperm Morphology Classification Using Deep Neural Networks: Enhancing Diagnostic Accuracy in Male Fertility Assessment

Abstract

Sperm morphology, an essential parameter in male fertility evaluation, is traditionally assessed through manual microscopy, a process prone to observer variability and subjectivity. Recent advances in Artificial Intelligence (AI), particularly Deep Neural Networks (DNNs), have introduced more consistent, efficient, and accurate approaches to semen analysis. This study proposes an AI-assisted system for automated sperm morphology classification using a convolutional neural network (CNN) architecture trained on annotated sperm images. The model was evaluated on various morphological classes, including head, midpiece, and tail defects. Results show a significant improvement in classification accuracy compared to traditional methods, with the model achieving over 90% precision in identifying morphological abnormalities. The findings underscore the potential of AI as a reliable tool for clinical diagnostics in male infertility, reducing diagnostic errors and enabling large-scale semen evaluation. The study also discusses the challenges of dataset availability, interpretability, and ethical implications in deploying such technologies in reproductive health.

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