Deep learning-based approach for sperm morphology analysis

Abstract

Male infertility is a highly prevalent condition throughout the world. Sperm morphology analysis（SMA） is one of most important examination for evaluating male infertility. This paper highlights the strengths, limitations, and clinical applicability of conventional machine learning (ML) models and deep learning (DL) models in SMA from various studies. Simultaneously, we explore the potential role of segmentation and classification of complete sperm structure based on deep learning algorithms. Therefore, this narrative literature review aims to summarize the current evidence of artificial intelligence and machine learning applications for sperm morphology analysis and explore further recommendations about deep learning algorithms applications to practically enhance the performance.