Hsu Chen-Hao, Yeh Chun-Fu, Huang I-Shen, Chen Wei-Jen, Peng Yu-Ching, Tsai Cheng-Han, Ko Mong-Chi, Su Chun-Ping, Chen Hann-Chyun, Wu Wei-Lin, Liu Tyng-Luh, Lee Kuang-Min, Li Chiao-Hsuan, Tu Ethan, Huang William J
Department of Urology, Taipei Veterans General Hospital, Taipei, Taiwan.
Department of Urology, School of Medicine, College of Medicine and Shu-Tien Urological Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
J Assist Reprod Genet. 2024 Nov;41(11):3179-3187. doi: 10.1007/s10815-024-03215-5. Epub 2024 Sep 3.
Identification of mature sperm at microdissection testicular sperm extraction (mTESE) is a crucial step of sperm retrieval to help patients with non-obstructive azoospermia (NOA) proceed to intracytoplasmic sperm injection. Touch print smear (TPS) cytology allows immediate interpretation and prompt sperm identification intraoperatively. In this study, we leverage machine learning (ML) to facilitate TPS reading and conquer the learning curve for new operators.
One hundred seventy-six microscopic TPS images from the testicular specimen of patients with azoospermia at Taipei Veterans General Hospital were retrospectively collected, including categories of Sertoli cell, primary spermatocytes, round spermatids, elongated spermatids, immature sperm, and mature sperm. Among them, 118 images were assigned as the training set and 29 images as the validation set. RetinaNet (Lin et al. in IEEE Trans Pattern Anal Mach Intell. 42:318-327, 2020), a one-stage detection framework, was adopted for cell detection. The performance was evaluated at the cell level with average precision (AP) and recall, and the precision-recall (PR) curve was displayed among an independent testing set that contains 29 images that aim to assess the model.
The training set consisted of 4772 annotated cells, including 1782 Sertoli cells, 314 primary spermatocytes, 443 round spermatids, 279 elongated spermatids, 504 immature sperm, and 1450 mature sperm. This study demonstrated the performance of each category and the overall AP and recall on the validation set, which were 80.47% and 96.69%. The overall AP and recall were 79.48% and 93.63% on the testing set, while increased to 85.29% and 93.80% once the post-meiotic cells were merged into one category.
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.
在显微切割睾丸取精术（mTESE）中识别成熟精子是获取精子的关键步骤，有助于非梗阻性无精子症（NOA）患者进行卵胞浆内单精子注射。触摸印片涂片（TPS）细胞学检查可在术中立即进行解读并迅速识别精子。在本研究中，我们利用机器学习（ML）来辅助TPS判读，并帮助新操作人员克服学习曲线。
回顾性收集台北荣民总医院无精子症患者睾丸标本的176张显微TPS图像，包括支持细胞、初级精母细胞、圆形精子细胞、长形精子细胞、未成熟精子和成熟精子类别。其中，118张图像被指定为训练集，29张图像为验证集。采用单阶段检测框架RetinaNet（Lin等人，《IEEE模式分析与机器智能汇刊》。42:318 - 327, 2020）进行细胞检测。在细胞水平上以平均精度（AP）和召回率评估性能，并在包含29张图像的独立测试集上展示精确召回率（PR）曲线，以评估模型。
训练集包含4772个标注细胞，包括1782个支持细胞、314个初级精母细胞、443个圆形精子细胞、279个长形精子细胞、504个未成熟精子和1450个成熟精子。本研究展示了验证集上各分类的性能以及总体AP和召回率，分别为80.47%和96.69%。测试集上的总体AP和召回率分别为79.48%和93.63%，而一旦减数分裂后细胞合并为一类，AP和召回率分别提高到85.29%和93.80%。
本研究提出了一种创新方法，利用ML方法辅助诊断NOA患者mTESE时的精子发生情况。借助ML技术，外科医生能够确定精子发生阶段，并为不育男性提供及时的组织病理学诊断。
