Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
BMC Urol. 2024 Jul 29;24(1):156. doi: 10.1186/s12894-024-01537-1.
The relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Additive exPlanation (SHAP) association of surgical sperm retrieval from testes of different etiologies.
A total of 345 infertile couples who underwent intracytoplasmic sperm injection (ICSI) treatment with surgical sperm retrieval due to different etiologies from February 2020 to March 2023 at the reproductive center were retrospectively analyzed. The six machine learning (ML) models were used to predict the clinical pregnancy of ICSI. After evaluating the performance characteristics of the six ML models, the Extreme Gradient Boosting model (XGBoost) was selected as the best model, and SHAP was utilized to interpret the XGBoost model for predicting clinical pregnancies and to reveal the decision-making process of the model.
Combining the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, brier score, and the area under the precision-recall (P-R) curve (AP), the XGBoost model has the best performance (AUROC: 0.858, 95% confidence interval (CI): 0.778-0.936, accuracy: 79.71%, brier score: 0.151). The global summary plot of SHAP values shows that the female age is the most important feature influencing the model output. The SHAP plot showed that younger age in females, bigger testicular volume (TV), non-tobacco use, higher anti-müllerian hormone (AMH), lower follicle-stimulating hormone (FSH) in females, lower FSH in males, the temporary ejaculatory disorders (TED) group, and not the non-obstructive azoospermia (NOA) group all resulted in an increased probability of clinical pregnancy.
The XGBoost model predicts clinical pregnancies associated with testicular sperm retrieval of different etiologies with high accuracy, reliability, and robustness. It can provide clinical counseling decisions for patients with surgical sperm retrieval of various etiologies.
不同病因的手术取精与临床妊娠的关系尚不清楚。我们旨在开发一种强大且可解释的机器学习（ML）模型，使用睾丸不同病因的手术取精的 SHapley Additive exPlanation（SHAP）关联来预测临床妊娠。
回顾性分析了 2020 年 2 月至 2023 年 3 月在生殖中心因不同病因接受手术取精行胞浆内单精子注射（ICSI）治疗的 345 对不孕夫妇。使用 6 种机器学习（ML）模型预测 ICSI 的临床妊娠。在评估了 6 种 ML 模型的性能特征后，选择极端梯度提升模型（XGBoost）作为最佳模型，并使用 SHAP 对 XGBoost 模型进行解释，以预测临床妊娠，并揭示模型的决策过程。
结合接受者操作特征曲线下的面积（AUROC）、准确性、精度、召回率、F1 评分、Brier 评分和精度-召回率（P-R）曲线下的面积（AP），XGBoost 模型具有最佳性能（AUROC：0.858，95%置信区间（CI）：0.778-0.936，准确性：79.71%，Brier 评分：0.151）。SHAP 值的全局摘要图表明，女性年龄是影响模型输出的最重要特征。SHAP 图显示，女性年龄较小、睾丸体积（TV）较大、不吸烟、女性抗苗勒氏激素（AMH）较高、卵泡刺激素（FSH）较低、男性 FSH 较低、暂时性射精障碍（TED）组而不是非梗阻性无精子症（NOA）组，都导致临床妊娠的概率增加。
XGBoost 模型以高精度、高可靠性和稳健性预测不同病因睾丸取精的临床妊娠。它可以为各种病因的手术取精患者提供临床咨询决策。
