Machine learning-based prediction of pregnancy outcomes in couples with non-obstructive azoospermia using micro-TESE for ICSI: a retrospective cohort study  

作　　者：Lei Jia Pei-Gen Chen Li-Na Chen Cong Fang Jing Zhang Pan-Yu Chen 

机构地区：[1]Reproductive Medicine Center,The Sixth Affiliated Hospital,Sun Yat-sen University,Guangzhou 510275,China [2]GuangDong Engineering Technology Research Center ofFertility Preservation,The Sixth Affiliated Hospital,Sun Yatsen University,Guangzhou 510275,China [3]Biomedical Innovation Center,The Sixth Affliated Hospital,Sun Yat-sen University,Guangzhou 510275,China

出　　处：《Reproductive and Developmental Medicine》2024年第1期24-31,共8页生殖与发育医学（英文版）

基　　金：National Natural Science Foundation of China(82271651)。

摘　　要：Objective:To develop a clinically applicable tool for predicting clinical pregnancy,providing individualized patient counseling,and helping couples with non-obstructive azoospermia(NOA)decide whether to use fresh or cryopreserved spermatozoa for oocyte insemination before microdissection testicular sperm extraction(mTESE).Methods:A total of 240 couples with NOA who underwent mTESE-ICSI were divided into two groups based on the type of spermatozoa used for intracytoplasmic sperm injection(ICSI):the fresh and cryopreserved groups.After evaluating several machine learning algorithms,logistic regression was selected.Using LASSO regression and 10-fold cross-validation,the factors associated with clinical pregnancy were analyzed.Results:The area under the curves(AUCs)for the fresh and cryopreserved groups in the Logistic Regression-based prediction model were 0.977 and 0.759,respectively.Compared with various modeling algorithms,Logistic Regression outperformed machine learning in both groups,with an AUC of 0.945 for the fresh group and 0.788 for the cryopreserved group.Conclusion:The model accurately predicted clinical pregnancies in NOA couples.

关 键 词：Cryopreserved spermatozoa Fresh spermatozoa Logistic regression Microdissection testicular sperm extraction 

分 类 号：R698.2[医药卫生—泌尿科学]