Development of a deep-learning model for detecting positive tubules during sperm recovery for nonobstructive azoospermia

in Reproduction
Authors:
Teppei Takeshima Department of Urology, Reproduction Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan

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https://orcid.org/0000-0003-2733-5487
,
Jurii Karibe Department of Urology, Reproduction Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan

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Shinnosuke Kuroda Department of Urology, Reproduction Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan

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Yasushi Yumura Department of Urology, Reproduction Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan

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Correspondence should be addressed to T Takeshima: teppei_t@yokohama-cu.ac.jp
DOI:
https://doi.org/10.1530/REP-24-0181
Volume/Issue:
Volume 168: Issue 4
Article ID:
e240181
Article Type:
Review Article
Online Publication Date:
27 Aug 2024
Copyright:
© the author(s) 2024
Restricted access
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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.

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Sept 2018 onwards Past Year Past 30 Days
Full Text Views 10 10 9
PDF Downloads 12 12 11

 

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Print ISSN:
1470-1626
Online ISSN:
1741-7899

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