Artificial intelligence interpretation of touch print smear cytology of testicular specimen from patients with azoospermia.

Chen-Hao HsuChun-Fu YehI-Shen HuangWei-Jen ChenYu-Ching PengCheng-Han TsaiMong-Chi KoChun-Ping SuHann-Chyun ChenWei-Lin WuTyng-Luh LiuKuang-Min LeeChiao-Hsuan LiEthan TuWilliam J Huang

Published in: Journal of assisted reproduction and genetics (2024)

This study proposed an innovative approach that leveraged ML methods to facilitate the diagnosis of spermatogenesis at mTESE for patients with NOA. With the assistance of ML techniques, surgeons could determine the stages of spermatogenesis and provide timely histopathological diagnosis for infertile males.

Keyphrases

artificial intelligence
machine learning
big data
deep learning
quality improvement
polycystic ovary syndrome
type diabetes
insulin resistance