Deep Learning for Semen Analysis in Male Infertility: Computer Vision, Multimodal Fusion, and Clinical Translation

2026-07-06

AI Summary

This study addresses the limitations of conventional semen analysis—such as subjectivity and low efficiency—which hinder precise diagnosis and treatment of male infertility. The work proposes a comprehensive framework for deep learning–driven intelligent sperm analysis, progressing from task-oriented visual recognition (including detection, segmentation, tracking, and classification) to trustworthy multimodal reproductive intelligence. This framework integrates microscopic images, time-series videos, CASA parameters, DNA integrity metrics, and clinical metadata, while incorporating privacy preservation and model interpretability. The paper systematically reviews public datasets, evaluation metrics, and benchmark methods, identifies key non-algorithmic factors critical for clinical translation beyond raw performance, and outlines a staged roadmap for real-world deployment, thereby offering a standardized, verifiable, and clinically viable AI-enabled solution for semen analysis.

Abstract

Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by inter- and intra-observer variability, motivating the development of objective and reproducible computational approaches. This review provides a comprehensive and perspective-oriented synthesis of artificial intelligence-driven sperm analysis, with a focus on computer vision, deep learning, multimodal fusion, robustness, and clinical translation. We first review task-specific methods for sperm detection and counting, tracking-based motility assessment, semantic and instance segmentation, morphology and defect classification, functional assessment, and genetic integrity evaluation. We then summarize public datasets, benchmarks, evaluation metrics, and emerging multimodal strategies that integrate microscopic images, time-lapse videos, CASA-derived parameters, DNA integrity assays, and clinical metadata. Beyond algorithmic performance, we discuss key barriers to real-world deployment, including data scarcity, cross-center domain shift, annotation inconsistency, interpretability, uncertainty calibration, privacy-preserving learning, and workflow integration. Finally, we outline a staged clinical translation roadmap spanning technical standardization, multicenter retrospective validation, silent prospective evaluation, human-in-the-loop clinical testing, ART outcome validation, regulatory approval, and post-market monitoring. By organizing the field from task-specific visual recognition to trustworthy multimodal reproductive intelligence, this review highlights both the progress and the unresolved challenges required to translate AI-driven sperm analysis into clinically meaningful decision support.

Problem

• male infertility
• semen analysis
• inter-observer variability
• computer vision
• clinical translation

Innovation

• multimodal fusion
• computer vision
• clinical translation
• deep learning
• sperm analysis