Authors
Gwidong Han, Seung Pyo Hong, Seung Jae Lee, Chunghee Cho
Published in
Andrology. Sep 21, 2026. Epub Sep 21, 2026.
Abstract
Mammalian sperm head morphology varies across species and dictates fertilization capability. Rodent sperm have asymmetric, hook-shaped heads, complicating analysis. Despite widespread biomedical use, current tools poorly characterize these complex variations.
This study aimed to establish a robust framework for the precise, noninvasive, and quantitative analysis of mouse sperm morphology.
We developed ADAM-net (Anomaly-aware Deep-learning Architecture for Morphology), a machine-learning system for mouse sperm head and neck structures. Integrating a dataset preparation module, ResNet-18, and additional layers, it features two variants: ADAM-net-FL for fluorescence (FL) and ADAM-net-BF for bright-field (BF) images.
ADAM-net-BF enables simultaneous analysis of the head and neck using noninvasively acquired BF images. Atypical head shapes were frequently associated with a neck in which the tail is attached perpendicular to the head axis, revealing a specific relationship between head shape and tail attachment pattern. Additionally, ADAM-net provided novel quantitative insights into testicular germ cell-specific HSF2-interacting lncRNA (Teshl)-knockout sperm defects, supporting its utility for mouse sperm morphology analysis.
ADAM-net provides a standard and facile tool that will have applications in various reproductive studies, extending to the exploration of subtle structural dynamics such as the sperm head-tail alignment.
PMID:
42765446
Bibliographic data and abstract were imported from PubMed on 21 Sep 2026.
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