Year: 2025
Type: article
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.
Source: BMC Urology
Authors: Bianping Liang, Mingxue Wang
Institutions: Chongqing University, Chongqing Emergency Medical Center
Language: English
Subfield: Reproductive Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W4415379670