Department of Urology, Reproduction Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan.
Reproduction. 2024 Aug 27;168(4). doi: 10.1530/REP-24-0181. Print 2024 Oct 1.
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.
为了提高非梗阻性无精子症患者的手术睾丸精子获取效果，开发了一种深度学习模型，通过对微切割睾丸精子提取过程中采集的含有精子的小管进行标记，用 110 张图像对其进行训练和验证，以此来识别生精小管。经过训练，该模型的平均准确率为 0.60。
