Integrated subcellular localization of functional fluorescence probes and functional analysis in motile spermatozoa by an AI-enhanced algorithm.

Abstract

In the evaluation of male infertility, precise assessment of sperm functional competence has surpassed the requirements of conventional semen parameters. Existing computer-aided analysis systems are deficient at the molecular diagnostic level and also face challenges in live-cell fluorescence quantification. To address these issues, we have developed a novel integrated computational-imaging platform that combines a fine-tuned You Only Look Once version 8 (YOLOv8) architecture, tailored for the EVISEN dataset, with dual-probe fluorescence microscopy image segmentation, enabling simultaneous quantification of intracellular pH (pHi) and mitochondrial DNA G-quadruplexes (mtDNA G4s). By automating the localization of fluorescent foci, our algorithm systematically discriminates between the fluorescent signatures of the sperm head and principal piece, revealing correlations between fluorescence intensity ratios and sperm functional outcomes. This study demonstrates the potential of artificial intelligence (AI)-enhanced multimodal sperm analysis for molecular phenotyping of sperm functional competence. Integrating deep learning with live-cell fluorescence imaging, our platform offers a transformative tool for mechanistically informed diagnostics of male infertility.