Construction and validation of a risk scoring system for moderate to severe spermatogenic dysfunction based on scrotal ultrasound parameters and machine learning
10.3760/cma.j.cn101441-20231124-00336
- VernacularTitle:基于阴囊超声参数及机器学习的中重度生精功能障碍风险评分系统的构建与验证
- Author:
Meng WU
1
;
Xuefeng LI
;
Xinyan LI
;
Feifei LIU
;
Jie YANG
;
Guanghe CUI
Author Information
1. 滨州医学院附属医院超声医学科，滨州 256603
- Publication Type:Journal Article
- Keywords:
Spermatogenic dysfunction;
Machine learning;
SHAP;
Scrotal ultrasound
- From:
Chinese Journal of Reproduction and Contraception
2024;44(7):723-727
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a risk scoring system for moderate to severe spermatogenic dysfunction based on scrotal ultrasound parameters and machine learning, and to explore its value.Methods:A retrospective cohort analysis was conducted on 112 patients diagnosed with azoospermia, moderate to severe oligospermia, and asthenospermia in the Department of Reproductive Medicine at Binzhou Medical University Affiliated Hospital from June 2021 to December 2022. Scrotal ultrasound parameters of these patients were compared with those of 116 normal male patients who visited the same hospital during the same period for reproductive assistance. Models were constructed using Random Forest, Support Vector Machine, logistic Regression, K-nearest neighbor algorithm, and XGBoost. A risk scoring system was established based on the average SHapley Additive exPlanation values of each model. The predictive performance of the model was evaluated using the receiver operating characteristic curve, and the clinical application value of the model was evaluated using the decision curve analysis.Results:The scoring system included bilateral testicular total volume, whether the testicular echo was uniform, the inner diameter of the right spermatic vein, and the reflux time of varicocele. The area under the curve (AUC) of the risk scoring system for the training set was 0.757, and the AUC for the test set was 0.718. The decision curve showed that this scoring system had a high clinical value.Conclusion:A risk scoring system based on scrotal ultrasound parameters and machine learning can effectively predict moderate to severe spermatogenic dysfunction, which is of positive significance for early detection of such patients.
