Prediction of testicular histology in azoospermia patients through deep learning-enabled two-dimensional grayscale ultrasound  

作　　者：Jia-Ying Hu Zhen-Zhe Lin Li Ding Zhi-Xing Zhang Wan-Ling Huang Sha-Sha Huang Bin Li Xiao-Yan Xie Ming-De Lu Chun-Hua Deng Hao-Tian Lin Yong Gao Zhu Wang 

机构地区：[1]Department of Medical Ultrasonics,Institute of Diagnostic and Interventional Ultrasound,The First Affiliated Hospital of Sun Yat-sen University,Guangzhou 510080,China [2]State Key Laboratory of Ophthalmology,Zhongshan Ophthalmic Center,Sun Yat-sen University,Guangzhou 510060,China [3]Department of Pathology,The First Affiliated Hospital of Sun Yat-sen University,Guangzhou 510080,China [4]Department of Ultrasonography,NHC Key Laboratory of Male Reproduction and Genetics,Guangdong Provincial Reproductive Science Institute(Guangdong Provincial Fertility Hospital),Human Sperm Bank of Guangdong Province,Guangzhou 510062,China [5]Department of Medical Ultrasonics,The Eighth Affiliated Hospital of Sun Yat-sen University,Shenzhen 518033,China [6]Clinical Trial Unit,The First Affiliated Hospital of Sun Yat-sen University,Guangzhou 510080,China [7]Department of Urology and Andrology,The First Affiliated Hospital of Sun Yat-sen University,Guangzhou 510080,China [8]Center for Precision Medicine,Sun Yat-sen University,Guangzhou 510080,China [9]Reproductive Medicine Center,The Key Laboratory for Reproductive Medicine of Guangdong Province,The First Affiliated Hospital of Sun Yat-sen University,Guangzhou 510080,China

出　　处：《Asian Journal of Andrology》2025年第2期254-260,共7页亚洲男性学杂志(英文版)

基　　金：supported by the Key Research and Development Program of Guangzhou Science and Technology Projects(No.2023B03J1260).

摘　　要：Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia.Therefore,we developed a deep learning(DL)model to establish the associations between testicular grayscale ultrasound images and testicular histology.We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia(353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University,Guangzhou,China)to develop a DL model.We obtained testicular histology during conventional testicular sperm extraction.Our DL model was trained based on ultrasound images or fusion data(ultrasound images fused with the corresponding testicular volume)to distinguish spermatozoa presence in pathology(SPP)and spermatozoa absence in pathology(SAP)and to classify maturation arrest(MA)and Sertoli cell-only syndrome(SCOS)in patients with SAP.Areas under the receiver operating characteristic curve(AUCs),accuracy,sensitivity,and specificity were used to analyze model performance.DL based on images achieved an AUC of 0.922(95%confidence interval[CI]:0.908-0.935),a sensitivity of 80.9%,a specificity of 84.6%,and an accuracy of 83.5%in predicting SPP(including normal spermatogenesis and hypospermatogenesis)and SAP(including MA and SCOS).In the identification of SCOS and MA,DL on fusion data yielded better diagnostic performance with an AUC of 0.979(95%CI:0.969-0.989),a sensitivity of 89.7%,a specificity of 97.1%,and an accuracy of 92.1%.Our study provides a noninvasive method to predict testicular histology for patients with azoospermia,which would avoid unnecessary testicular biopsy.

关 键 词：AZOOSPERMIA deep learning male infertility testicular histology ULTRASOUND 

分 类 号：R69[医药卫生—泌尿科学]