Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, 430030, China.
Reprod Biol Endocrinol. 2021 Apr 5;19(1):53. doi: 10.1186/s12958-021-00734-z.
To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing.
This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.
For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree.
Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.
为了最大限度地降低体外受精（IVF）相关的多胚胎妊娠率，已经做出了巨大努力。之前与 IVF 相关的机器学习研究主要集中在选择优质胚胎以提高结果上，然而，对于预后不佳或中等或低质量胚胎的患者，选择单胚胎移植（SET）和双胚胎移植（DET）可能会令人困惑。
这是一项应用研究，纳入了 2016 年至 2018 年期间在中国武汉同济医院接受治疗的 9211 名患者的 10076 个胚胎。使用机器学习系统 XGBoost 建立了一个层次模型，同时学习胚胎着床潜力和双胚胎移植（DET）的影响。使用 ROC 曲线的 AUC 评估模型的性能。还对 19 个选定特征进行了多元回归分析，以证明预测特征重要性和与结果的统计学关系之间的差异。
对于单胚胎移植（SET）妊娠，以下变量仍然显著：年龄、IVF 尝试次数、HCG 日雌二醇水平和子宫内膜厚度。对于 DET 妊娠，年龄、IVF 尝试次数、子宫内膜厚度和新添加的 P1+P2 仍然显著。对于 DET 双胞胎风险，年龄、IVF 尝试次数、2PN/MII 和 P1×P2 仍然显著。该算法重复 30 次，SET 妊娠、DET 妊娠和 DET 双胞胎风险的平均 AUC 分别为 0.7945、0.8385 和 0.7229。妊娠和双胞胎风险的预测和观察率趋势基本一致。XGBoost 优于其他两种算法：逻辑回归和分类回归树。
基于确定因素加权分析的人工智能可以为任何给定患者提供个体化的胚胎选择策略，并预测临床妊娠率和双胞胎风险，从而优化临床结果。
