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一种预测第二次显微外科睾丸精子提取成功率的机器学习算法。

A machine learning algorithm to predict the success of a second microsurgical testicular sperm extraction.

作者信息

Obeidat Akef, Sabbah Belal Nedal, Mandourah Hammam, Alghafees Mohammad, Sabbah Ahmad Nedal, Hajja Amro, Ouban Abdurrahman, Alkattan Wael, Omar Mohammed Ali, Abdalla Hytham Mubarak, Abuzubida Abdalrahman, Abbasi Safwan Urooj, Alkhneizan Zeyad, Alhussain Fahad, Alsaleh Faisal, Kattan Said, Alhathal Naif

机构信息

Alfaisal University, College of Medicine, Riyadh, Saudi Arabia.

Urology, King Abdulaziz University Hospital, Jeddah, Saudi Arabia.

出版信息

Ann Med Surg (Lond). 2025 Jul 10;87(9):5394-5400. doi: 10.1097/MS9.0000000000003560. eCollection 2025 Sep.

Abstract

INTRODUCTION

Testicular sperm extraction (TESE) is a common procedure for retrieving sperm in men with azoospermia. However, the success rates of a second TESE following an initial unsuccessful attempt remain low. This study aims to develop and evaluate a machine learning algorithm to predict the success of a second microsurgical TESE (microTESE).

METHODS

Medical records of 47 patients who underwent a second microTESE were analyzed. The dataset included variables such as procedure side, histopathology, preoperative Follicle-stimulating hormone (FSH) and testosterone levels, testicular volume, and comorbidities. Supervised machine learning algorithms, including support vector machine (SVM), were employed to predict the success of the second microTESE. The dataset was split into training (80%) and testing (20%) sets.

RESULTS

The SVM model achieved an accuracy of 80% after hyperparameter tuning. Bilateral procedures and longer intervals between surgeries were associated with higher success rates, while a history of cancer correlated with negative outcomes. FSH and testosterone levels were also identified as predictive factors. The SVM model's feature importance analysis highlighted histopathology, varicocele, hormone levels, and the interval between procedures as highly correlated with the success of a second microTESE.

DISCUSSION

The machine learning model accurately predicted the presence or absence of spermatozoa in patients with non-obstructive azoospermia undergoing a second microTESE. The findings are consistent with previous studies and provide valuable insights into the predictive factors for the success of a second microTESE. However, the study's limitations include selection bias and reliance on retrospective data.

CONCLUSION

The SVM model shows promise in predicting the success of a second microTESE by incorporating factors such as age, hormonal levels, testicular volume, and genetic evaluation. Further validation and refinement are needed to ensure the model's accuracy and applicability across different populations.

摘要

引言

睾丸精子提取术(TESE)是无精子症男性获取精子的常用方法。然而,首次尝试失败后再次进行TESE的成功率仍然较低。本研究旨在开发并评估一种机器学习算法,以预测第二次显微外科睾丸精子提取术(microTESE)的成功率。

方法

分析了47例行第二次microTESE患者的病历。数据集包括手术侧别、组织病理学、术前促卵泡生成素(FSH)和睾酮水平、睾丸体积及合并症等变量。采用包括支持向量机(SVM)在内的监督机器学习算法来预测第二次microTESE的成功率。数据集被分为训练集(80%)和测试集(20%)。

结果

经过超参数调整后,SVM模型的准确率达到80%。双侧手术以及手术间隔时间较长与较高的成功率相关,而癌症病史与不良结果相关。FSH和睾酮水平也被确定为预测因素。SVM模型的特征重要性分析突出显示组织病理学、精索静脉曲张、激素水平以及手术间隔时间与第二次microTESE的成功率高度相关。

讨论

该机器学习模型准确预测了接受第二次microTESE的非梗阻性无精子症患者精子的有无。研究结果与先前的研究一致,并为第二次microTESE成功的预测因素提供了有价值的见解。然而,该研究的局限性包括选择偏倚以及对回顾性数据的依赖。

结论

SVM模型通过纳入年龄、激素水平、睾丸体积和基因评估等因素,在预测第二次microTESE的成功率方面显示出前景。需要进一步验证和完善,以确保该模型在不同人群中的准确性和适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc4e/12401292/3748ddbcda75/ms9-87-5394-g001.jpg

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