{"title":"开发一种深度学习模型，用于检测非梗阻性无精子症患者精子复苏过程中的阳性小管。","authors":"Teppei Takeshima, Jurii Karibe, Shinnosuke Kuroda, Yasushi Yumura","doi":"10.1530/REP-24-0181","DOIUrl":null,"url":null,"abstract":"<p><p>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 microdissection testicular sperm extraction as training and validation data. After training, the model achieved an average precision of 0.60.</p>","PeriodicalId":21127,"journal":{"name":"Reproduction","volume":" ","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of a deep-learning model for detecting positive tubules during sperm recovery for nonobstructive azoospermia.\",\"authors\":\"Teppei Takeshima, Jurii Karibe, Shinnosuke Kuroda, Yasushi Yumura\",\"doi\":\"10.1530/REP-24-0181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 microdissection testicular sperm extraction as training and validation data. After training, the model achieved an average precision of 0.60.</p>\",\"PeriodicalId\":21127,\"journal\":{\"name\":\"Reproduction\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Reproduction\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1530/REP-24-0181\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/1 0:00:00\",\"PubModel\":\"Print\",\"JCR\":\"Q1\",\"JCRName\":\"DEVELOPMENTAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reproduction","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1530/REP-24-0181","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/1 0:00:00","PubModel":"Print","JCR":"Q1","JCRName":"DEVELOPMENTAL BIOLOGY","Score":null,"Total":0}
Development of a deep-learning model for detecting positive tubules during sperm recovery for nonobstructive azoospermia.
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 microdissection testicular sperm extraction as training and validation data. After training, the model achieved an average precision of 0.60.
期刊介绍：
Reproduction is the official journal of the Society of Reproduction and Fertility (SRF). It was formed in 2001 when the Society merged its two journals, the Journal of Reproduction and Fertility and Reviews of Reproduction.
Reproduction publishes original research articles and topical reviews on the subject of reproductive and developmental biology, and reproductive medicine. The journal will consider publication of high-quality meta-analyses; these should be submitted to the research papers category. The journal considers studies in humans and all animal species, and will publish clinical studies if they advance our understanding of the underlying causes and/or mechanisms of disease.
Scientific excellence and broad interest to our readership are the most important criteria during the peer review process. The journal publishes articles that make a clear advance in the field, whether of mechanistic, descriptive or technical focus. Articles that substantiate new or controversial reports are welcomed if they are noteworthy and advance the field. Topics include, but are not limited to, reproductive immunology, reproductive toxicology, stem cells, environmental effects on reproductive potential and health (eg obesity), extracellular vesicles, fertility preservation and epigenetic effects on reproductive and developmental processes.
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