Artificial Intelligence Based Machine Learning Models Predict Sperm parameter Upgrading after Varicocele Repair: A Multi-Institutional Analysis

Purpose: Varicocele repair is recommended in the presence of a clinical varicocele together with at least one abnormal semen parameter, and male infertility. Unfortunately, up to 50% of men who meet criteria for repair will not see meaningful benefit in outcomes despite successful treatment. We developed an artificial intelligence (AI) model to predict which men with varicocele will benefit from treatment. Materials and Methods: We identified men with infertility, clinical varicocele, and at least one abnormal semen parameter from two large urology centers in North America (Miami and Toronto) between 2006 and 2020. We collected pre and postoperative clinical and hormonal data following treatment. Clinical upgrading was defined as an increase in sperm concentration that would allow a couple to access previously unavailable reproductive options. The tiers used for upgrading were: 1–5 million/mL (ICSI/IVF), 5–15 million/mL (IUI) and >15 million/mL (natural conception). Thus moving from ICSI/IVF to IUI, or from IUI to natural conception, would be considered an upgrade. AI models were trained and tested using R to predict which patients were likely to upgrade after surgery. The model sorted men into categories that defined how likely they were to upgrade after surgery (likely, equivocal, and unlikely). Results: Data from 240 men were included from both centers. A total of 45.6% of men experienced an upgrade in sperm concentration following surgery, 48.1% did not change, and 6.3% downgraded. The data from Miami were used to create a random forest model for predicting upgrade in sperm concentration. On external validation using Toronto data, the model accurately predicted upgrade in 87% of men deemed likely to improve, and in 49% and 36% of men who were equivocal and unlikely to improve, respectively. Overall, the personalized prediction for patients in the validation cohort was accurate (AUC 0.72). Conclusions: A machine learning model performed well in predicting clinically meaningful post-varicocelectomy sperm parameters using pre-operative hormonal, clinical, and semen analysis data. To our knowledge, this is the first prediction model to show the utility of hormonal data, as well as the first to use machine learning models to predict clinically meaningful upgrading. This model will be published online as a clinical calculator that can be used in the preoperative counseling of patients.研究目的：对于合并临床型精索静脉曲张、至少一项精液参数异常以及男性不育的患者，推荐行精索静脉曲张修复术。然而，即便手术成功，仍有多达50%符合手术指征的患者无法获得具有临床意义的治疗获益。本研究开发了一款人工智能（AI）模型，用于预测精索静脉曲张患者能否从手术治疗中获益。 研究对象与方法：我们纳入了2006年至2020年间，来自北美两家大型泌尿外科中心（迈阿密与多伦多）的不育、临床型精索静脉曲张且至少一项精液参数异常的男性患者。收集患者术前及术后的临床与激素数据。本研究将“临床获益升级”定义为精子浓度提升，使夫妇能够获得此前无法企及的生殖手段：升级层级分为1~5百万/mL（可开展卵胞浆内单精子注射/体外受精-胚胎移植，ICSI/IVF）、5~15百万/mL（可开展宫腔内人工授精，IUI）以及＞15百万/mL（可自然受孕）。即从ICSI/IVF层级升级至IUI层级，或从IUI层级升级至自然受孕层级，均视为获益升级。我们使用R语言训练并测试AI模型，以预测患者术后能否实现获益升级。该模型将患者分为三类，以明确其术后升级的可能性：高可能性、不确定性与低可能性。 研究结果：本研究共纳入两家中心的240例男性患者。术后共有45.6%的患者实现精子浓度升级，48.1%的患者精子浓度无明显变化，6.3%的患者出现精子浓度下降。我们使用迈阿密中心的数据构建了用于预测精子浓度升级的随机森林模型，并采用多伦多中心的数据进行外部验证。结果显示，该模型对被判定为高可能性升级的患者预测准确率达87%，对不确定性与低可能性升级的患者预测准确率分别为49%与36%。验证队列中，患者的个体化预测整体准确率良好（受试者工作特征曲线下面积（AUC）=0.72）。 研究结论：基于术前激素、临床及精液分析数据，本机器学习模型可较好地预测精索静脉曲张术后患者的精子浓度临床获益升级情况。据我们所知，本研究首次证实了激素数据在该预测模型中的应用价值，同时也是首个采用机器学习模型预测临床意义上的获益升级的研究。本模型将以临床计算器的形式在线发布，用于患者的术前咨询。