Kayra Mehmet Vehbi, Şahin Ali, Toksöz Serdar, Serindere Mehmet, Altıntaş Emre, Özer Halil, Gül Murat
Department of Urology, Adana Dr. Turgut Noyan Application and Research Center, Baskent University, Adana, Turkey.
Department of Emergency Service, Konya Dr. Vefa Tanır State Hospital, Konya, Turkey.
Andrology. 2024 Oct 3. doi: 10.1111/andr.13776.
Varicocoele is a correctable cause of male infertility. Although physical examination is still being used in diagnosis and grading, it gives conflicting results when compared to ultrasonography-based varicocoele grading.
We aimed to develop a multi-class machine learning model for the grading of varicocoeles based on ultrasonographic measurements.
Between January and May 2024, we enrolled unilateral varicocoele patients at an infertility clinic, assessing their varicocoele stages using the Dubin and Amelar system. We measured vascular diameter and reflux time at the testicular apex and the subinguinal region ultrasonography in both the supine and standing positions. Using these measurements, we developed four multi-class machine learning models, evaluating their performance metrics and determining which patient position and projection were most influential in varicocoele grading.
We included 248 patients with unilateral varicocoele in the study, their average age was 26.61 ± 4.95 years old. Of these, 212 had left-sided and 36 had right-sided varicocoeles. According to the Dubin and Amelar system, there were 66 grade I, 96 grade II, and 86 grade III varicocoeles. Among the models we created, the random forest (RF) model performed best, with an overall accuracy of 0.81 ± 0.06, an F1 score of 0.79 ± 0.02, a sensitivity of 0.69 ± 0.02, and a specificity of 0.8 ± 0.03. Vascular diameter measurement at the testicular apex in the supine position had the most impact on grading across all models. In support vector machine and multi-layer perceptron models, reflux time measurements from the subinguinal projection in the standing position contributed the most, while in RF and k-nearest neighbors models, measurements from the subinguinal projection in the supine position were the most influential.
Machine learning methods have demonstrated superior accuracy in predicting disease compared to traditional statistical regressions and nomograms. These advancements hold promise for clinically automated prediction of varicocoele grades in patients. Tailored varicocoele grading for individuals has the potential to enhance treatment effectiveness and overall quality of life.
精索静脉曲张是男性不育的一个可纠正原因。尽管体格检查仍用于诊断和分级，但与基于超声的精索静脉曲张分级相比，其结果存在冲突。
我们旨在开发一种基于超声测量的精索静脉曲张分级多类机器学习模型。
在2024年1月至5月期间，我们在一家不育诊所招募了单侧精索静脉曲张患者，使用杜宾和阿梅拉尔系统评估他们的精索静脉曲张阶段。我们在仰卧位和站立位通过超声测量睾丸尖部和腹股沟下区域的血管直径和反流时间。利用这些测量数据，我们开发了四个多类机器学习模型，评估它们的性能指标，并确定哪些患者体位和投影对精索静脉曲张分级影响最大。
我们纳入了248例单侧精索静脉曲张患者进行研究，他们的平均年龄为26.61±4.95岁。其中，212例为左侧精索静脉曲张，36例为右侧精索静脉曲张。根据杜宾和阿梅拉尔系统，有66例I级、96例II级和86例III级精索静脉曲张。在我们创建的模型中，随机森林（RF）模型表现最佳，总体准确率为0.81±0.06，F1分数为0.79±0.02，灵敏度为0.69±0.02，特异性为0.8±0.03。仰卧位时睾丸尖部的血管直径测量对所有模型的分级影响最大。在支持向量机和多层感知器模型中，站立位腹股沟下投影的反流时间测量贡献最大，而在RF和k近邻模型中，仰卧位腹股沟下投影的测量最具影响力。
与传统统计回归和列线图相比，机器学习方法在预测疾病方面已显示出更高的准确性。这些进展有望实现临床上对患者精索静脉曲张等级的自动预测。为个体量身定制精索静脉曲张分级有可能提高治疗效果和整体生活质量。
