AIMC Topic: Seminiferous Tubules
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Deep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy.
Scientific reports
Jul 1, 2025
Infertility is a global issue, and approximately 50% of cases are due to male factors, with defective spermatogenesis being the main one. For studies of spermatogenesis, evaluating the seminiferous tubule stage is essential. However, current evaluati...
Development of a deep-learning model for detecting positive tubules during sperm recovery for nonobstructive azoospermia.
Reproduction (Cambridge, England)
Aug 27, 2024
To enhance surgical testicular sperm retrieval outcome for men with nonobstructive azoospermia, a deep-learning model was developed to identify positive seminiferous tubules by labeling 110 images with sperm-containing tubules sampled during microdis...
Deep Learning-Based Spermatogenic Staging in Tissue Sections of Cynomolgus Macaque Testes.
Toxicologic pathology
Mar 11, 2024
The indirect assessment of adverse effects on fertility in cynomolgus monkeys requires that tissue sections of the testis be microscopically evaluated with awareness of the stage of spermatogenesis that a particular cross-section of a seminiferous tu...
A method for utilizing automated machine learning for histopathological classification of testis based on Johnsen scores.
Scientific reports
May 10, 2021
We examined whether a tool for determining Johnsen scores automatically using artificial intelligence (AI) could be used in place of traditional Johnsen scoring to support pathologists' evaluations. Average precision, precision, and recall were asses...
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