Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada.
School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China.
Reprod Biol Endocrinol. 2024 Oct 28;22(1):132. doi: 10.1186/s12958-024-01302-x.
Blastocyst morphology has been demonstrated to be associated with ploidy status. Existing artificial intelligence models use manual grading or 2D images as the input for euploidy prediction, which suffer from subjectivity from observers and information loss due to incomplete features from 2D images. Here we aim to predict euploidy in human blastocysts using quantitative morphological parameters obtained by 3D morphology measurement.
Multi-view images of 226 blastocysts on Day 6 were captured by manually rotating blastocysts during the preparation stage of trophectoderm biopsy. Quantitative morphological parameters were obtained by 3D morphology measurement. Six machine learning models were trained using 3D morphological parameters as the input and PGT-A results as the ground truth outcome. Model performance, including sensitivity, specificity, precision, accuracy and AUC, was evaluated on an additional test dataset. Model interpretation was conducted on the best-performing model.
All the 3D morphological parameters were significantly different between euploid and non-euploid blastocysts. Multivariate analysis revealed that three of the five parameters including trophectoderm cell number, trophectoderm cell size variance and inner cell mass area maintained statistical significance (P < 0.001, aOR = 1.054, 95% CI 1.034-1.073; P = 0.003, aOR = 0.994, 95% CI 0.991-0.998; P = 0.010, aOR = 1.003, 95% CI 1.001-1.006). The accuracy of euploidy prediction by the six machine learning models ranged from 80 to 95.6%, and the AUCs ranged from 0.881 to 0.984. Particularly, the decision tree model achieved the highest accuracy of 95.6% (95% CI 84.9-99.5%) with the AUC of 0.978 (95% CI 0.882-0.999), and the extreme gradient boosting model achieved the highest AUC of 0.984 (95% CI 0.892-1.000) with the accuracy of 93.3% (95% CI 81.7-98.6%). No significant difference was found between different age groups using either decision tree or extreme gradient boosting to predict euploid blastocysts. The quantitative criteria extracted from the decision tree imply that euploid blastocysts have a higher number of trophectoderm cells, larger inner cell mass area, and smaller trophectoderm cell size variance compared to non-euploid blastocysts.
Using quantitative morphological parameters obtained by 3D morphology measurement, the decision tree-based machine learning model achieved an accuracy of 95.6% and AUC of 0.978 for predicting euploidy in Day 6 human blastocysts.
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囊胚形态已被证明与倍性状态有关。现有的人工智能模型使用手动分级或二维图像作为整倍体预测的输入，这两种方法都存在观察者的主观性和二维图像特征不完整导致的信息丢失的问题。本研究旨在使用通过三维形态测量获得的定量形态参数来预测人类囊胚的整倍体性。
在滋养外胚层活检准备阶段通过手动旋转囊胚，获取 226 个第 6 天囊胚的多视图图像。通过三维形态测量获得定量形态参数。使用三维形态参数作为输入，PGT-A 结果作为地面实况输出，训练了 6 种机器学习模型。在额外的测试数据集上评估模型性能，包括敏感性、特异性、精度、准确性和 AUC。对表现最佳的模型进行模型解释。
所有三维形态参数在整倍体和非整倍体囊胚之间均有显著差异。多元分析显示，五个参数中的三个参数，包括滋养外胚层细胞数量、滋养外胚层细胞大小方差和内细胞团面积，仍具有统计学意义（P<0.001，优势比 aOR=1.054，95%CI 1.034-1.073；P=0.003，aOR=0.994，95%CI 0.991-0.998；P=0.010，aOR=1.003，95%CI 1.001-1.006）。六种机器学习模型的整倍体预测准确率范围为 80%至 95.6%，AUC 范围为 0.881 至 0.984。特别是决策树模型的准确率最高，为 95.6%（95%CI 84.9-99.5%），AUC 为 0.978（95%CI 0.882-0.999），极端梯度增强模型的 AUC 最高，为 0.984（95%CI 0.892-1.000），准确率为 93.3%（95%CI 81.7-98.6%）。使用决策树或极端梯度增强来预测整倍体囊胚，不同年龄组之间没有发现显著差异。决策树提取的定量标准表明，与非整倍体囊胚相比，整倍体囊胚具有更多的滋养外胚层细胞、更大的内细胞团面积和更小的滋养外胚层细胞大小方差。
使用通过三维形态测量获得的定量形态参数，基于决策树的机器学习模型对第 6 天人类囊胚的整倍体性预测达到了 95.6%的准确率和 0.978 的 AUC。
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