Abstract
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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