Deep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy
Abstract 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 evaluation methods involve labor-intensive manual...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-06727-x |
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| author | Yuta Tokuoka Tsutomu Endo Takashi Morikura Yuki Hiradate Masahito Ikawa Akira Funahashi |
| author_facet | Yuta Tokuoka Tsutomu Endo Takashi Morikura Yuki Hiradate Masahito Ikawa Akira Funahashi |
| author_sort | Yuta Tokuoka |
| collection | DOAJ |
| description | Abstract 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 evaluation methods involve labor-intensive manual tasks with a lack of reproducibility owing to the subjective nature of visual evaluation by experts. Here, we propose a deep-learning-based method for automatically and objectively evaluating the seminiferous tubule stage. Our approach predicts which of 12 seminiferous tubule stages is represented in bright-field microscopic images of mouse seminiferous tubules stained by hematoxylin-PAS. The maximum prediction accuracy of our approach was 79.58% which rose to 98.33% with allowance for a prediction error of $$\pm 1$$ stage. Remarkably, although the model was not explicitly trained on the stage transition patterns, it inferred the patterns involved in the spermatogenesis. This method not only advances our understanding of spermatogenesis but also holds promise for improving the automated diagnosis of infertility. |
| format | Article |
| id | doaj-art-9765bc0da65e4242a6adb1ca45116af3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9765bc0da65e4242a6adb1ca45116af32025-08-20T03:45:28ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-06727-xDeep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopyYuta Tokuoka0Tsutomu Endo1Takashi Morikura2Yuki Hiradate3Masahito Ikawa4Akira Funahashi5Department of Biosciences and Informatics, Keio UniversityCenter for Experimental Animals, Research Facility Cluster, Tokyo Medical and Dental UniversityDepartment of Biosciences and Informatics, Keio UniversityResearch Institute for Microbial Diseases, Osaka UniversityResearch Institute for Microbial Diseases, Osaka UniversityDepartment of Biosciences and Informatics, Keio UniversityAbstract 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 evaluation methods involve labor-intensive manual tasks with a lack of reproducibility owing to the subjective nature of visual evaluation by experts. Here, we propose a deep-learning-based method for automatically and objectively evaluating the seminiferous tubule stage. Our approach predicts which of 12 seminiferous tubule stages is represented in bright-field microscopic images of mouse seminiferous tubules stained by hematoxylin-PAS. The maximum prediction accuracy of our approach was 79.58% which rose to 98.33% with allowance for a prediction error of $$\pm 1$$ stage. Remarkably, although the model was not explicitly trained on the stage transition patterns, it inferred the patterns involved in the spermatogenesis. This method not only advances our understanding of spermatogenesis but also holds promise for improving the automated diagnosis of infertility.https://doi.org/10.1038/s41598-025-06727-x |
| spellingShingle | Yuta Tokuoka Tsutomu Endo Takashi Morikura Yuki Hiradate Masahito Ikawa Akira Funahashi Deep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy Scientific Reports |
| title | Deep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy |
| title_full | Deep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy |
| title_fullStr | Deep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy |
| title_full_unstemmed | Deep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy |
| title_short | Deep-learning-based automated prediction of mouse seminiferous tubule stage by using bright-field microscopy |
| title_sort | deep learning based automated prediction of mouse seminiferous tubule stage by using bright field microscopy |
| url | https://doi.org/10.1038/s41598-025-06727-x |
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