Reproductive Medicine Center, Xiangya Hospital Central South University, Changsha, 410008, China.
J Ovarian Res. 2024 Aug 21;17(1):169. doi: 10.1186/s13048-024-01495-0.
Accurately predicting ovulation timing is critical for women undergoing natural cycle-frozen embryo transfer. However, the precise predicting of the ovulation timing remains challenging due to the lack of consensus among different clinics regarding the definition of this significant event.
To compare the effectiveness of preovulatory serum progesterone levels (P4) versus luteinizing hormone levels (LH) in predicting ovulation time using two machine learning models.
771 patients who underwent autologous natural cycle-frozen embryo transfer between January 2015 and February 2022 were recruited. Utilizing variables including follicle diameters, preovulatory serum levels of LH, E2, and P4, two machine learning models were constructed to predict the ovulation time, the importance of the variables in predicting ovulation timing was further ranked.
Two machine learning models have the capability to accurately predict the timing of ovulation, specifically within 72, 48, or 24 h. The overall accuracy rates of the validation dataset, as determined by the classification trees and random forest models, were found to be 78.83% and 85.28% respectively. Notably, when predicting ovulation within 24 h, the accuracy rate of P4 ≥ 0.65ng/ml exceeded 92%. Furthermore, it was important to consider LH or E2 levels in conjunction with P4 when assessing ovulation timing in cases where P4<0.65ng/ml.
Preovulatory serum P4 levels are better predictors of ovulation timing than LH levels and could be used as an alternative in clinical settings, and the model we developed can be used to pinpoint the day of ovulation. Ongoing research and advancements in technology are anticipated to enhance and refine the ovulation method.
准确预测排卵时间对于接受自然周期冷冻胚胎移植的女性至关重要。然而，由于不同诊所对这一重要事件的定义缺乏共识，因此准确预测排卵时间仍然具有挑战性。
比较两种机器学习模型中预排卵血清孕激素水平（P4）与黄体生成素水平（LH）预测排卵时间的效果。
招募了 2015 年 1 月至 2022 年 2 月期间接受自体自然周期冷冻胚胎移植的 771 例患者。利用卵泡直径、预排卵血清 LH、E2 和 P4 等变量，构建了两种机器学习模型来预测排卵时间，并进一步对预测排卵时间的变量重要性进行了排序。
两种机器学习模型都具有准确预测排卵时间的能力，特别是在 72、48 或 24 小时内。通过分类树和随机森林模型确定的验证数据集的总体准确率分别为 78.83%和 85.28%。值得注意的是，当预测 24 小时内排卵时，P4≥0.65ng/ml 的准确率超过 92%。此外，在 P4<0.65ng/ml 的情况下，评估排卵时间时，同时考虑 LH 或 E2 水平与 P4 水平非常重要。
预排卵血清 P4 水平是预测排卵时间比 LH 水平更好的指标，在临床环境中可以作为替代指标，我们开发的模型可用于确定排卵日。预计未来的研究和技术进步将增强和完善排卵方法。
