Garg Akhil, Bellver Jose, Bosch Ernesto, Remohí José Alejandro, Pellicer Antonio, Meseguer Marcos
IVIRMA Valencia, Spain.
IVIRMA Valencia, Spain; Health Research Institute la Fe, Valencia, Spain; Department of Pediatrics, Obstetrics and Gynecology, Faculty of Medicine, University of Valencia, Spain.
Reprod Biomed Online. 2025 Feb;50(2):104441. doi: 10.1016/j.rbmo.2024.104441. Epub 2024 Sep 4.
Can machine learning tools predict the number of metaphase II (MII) oocytes and trigger day at the start of the ovarian stimulation cycle?
A multicentre, retrospective study including 56,490 ovarian stimulation cycles (primary dataset) was carried out between 2020 and 2022 for analysis and feature selection. Of these, 13,090 were used to develop machine learning models for trigger day and the number of MII prediction, and another 5103 ovarian stimulation cycles (clinical validation dataset) from 2023 for clinical validation. Machine learning algorithms using deep learning were developed using optimal features from the primary dataset based on correlation.
A tool with two novel progressive machine learning algorithms using deep learning was able to predict the trigger day and number of MII oocytes: mean absolute error 1.60 (95% CI 1.56 to 1.64) and 3.75 (95% CI 3.65 to 3.86), respectively. The R value for the algorithm to predict the number of MII in the interquartile (Q3-Q1/P75-P25) range was 0.88; the entire dataset was 0.70 after removing the outliers at the planning phase of the stimulation cycle, which shows high accuracy. The interquartile root mean square error was 1.10 and 0.66 for the trigger day and the number of oocytes algorithm, respectively.
The tool using deep learning algorithms has high prediction power for trigger day and number of MII outcomes, and can be retrieved from patients at the start of the ovarian stimulation cycle; however, inclusion of more data and validation from different clinics are needed.
机器学习工具能否在卵巢刺激周期开始时预测中期II（MII）卵母细胞的数量和触发日？
在2020年至2022年期间进行了一项多中心回顾性研究，纳入56490个卵巢刺激周期（原始数据集）用于分析和特征选择。其中，13090个周期用于开发预测触发日和MII数量的机器学习模型，另外5103个来自2023年的卵巢刺激周期（临床验证数据集）用于临床验证。基于相关性，利用原始数据集中的最佳特征开发了使用深度学习的机器学习算法。
一种使用深度学习的具有两种新型渐进式机器学习算法的工具能够预测触发日和MII卵母细胞的数量：平均绝对误差分别为1.60（95%CI 1.56至1.64）和3.75（95%CI 3.65至3.86）。该算法预测四分位数间距（Q3-Q1/P75-P25）范围内MII数量的R值为0.88；在刺激周期规划阶段去除异常值后，整个数据集的R值为0.70，显示出较高的准确性。触发日算法和卵母细胞数量算法的四分位数均方根误差分别为1.10和0.66。
使用深度学习算法的该工具对触发日和MII结果数量具有较高的预测能力，并且可以在卵巢刺激周期开始时从患者处获取数据；然而，需要纳入更多数据并在不同诊所进行验证。
