通过定量三维形态测量无创预测人类囊胚的非整倍体:一项回顾性队列研究。

IF 4.2 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM Reproductive Biology and Endocrinology Pub Date : 2024-10-28 DOI:10.1186/s12958-024-01302-x
Guanqiao Shan, Khaled Abdalla, Hang Liu, Changsheng Dai, Justin Tan, Junhui Law, Carolyn Steinberg, Ang Li, Iryna Kuznyetsova, Zhuoran Zhang, Clifford Librach, Yu Sun
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引用次数: 0

摘要

背景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人工分级或二维图像作为非整倍体预测的输入,这受到观察者主观性和二维图像特征不完整导致的信息损失的影响。在此,我们旨在利用三维形态测量获得的定量形态参数来预测人类囊胚的非整倍体:方法:在滋养层活检的准备阶段,通过手动旋转囊胚采集了第 6 天 226 个囊胚的多视角图像。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 结果作为基本结果,训练了六个机器学习模型。在额外的测试数据集上评估了模型的性能,包括灵敏度、特异性、精确度、准确度和 AUC。根据表现最佳的模型进行模型解释:结果:所有三维形态参数在优倍囊胚和非优倍囊胚之间都有明显差异。多变量分析表明,在五个参数中,有三个参数(包括滋养层细胞数、滋养层细胞大小方差和内细胞团面积)保持了统计学意义(P 结论):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预测第 6 天人类囊胚的非整倍体的准确率达到 95.6%,AUC 达到 0.978:不适用。
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Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Trial registration: N/A.

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来源期刊
Reproductive Biology and Endocrinology
Reproductive Biology and Endocrinology 医学-内分泌学与代谢
CiteScore
7.90
自引率
2.30%
发文量
161
审稿时长
4-8 weeks
期刊介绍: Reproductive Biology and Endocrinology publishes and disseminates high-quality results from excellent research in the reproductive sciences. The journal publishes on topics covering gametogenesis, fertilization, early embryonic development, embryo-uterus interaction, reproductive development, pregnancy, uterine biology, endocrinology of reproduction, control of reproduction, reproductive immunology, neuroendocrinology, and veterinary and human reproductive medicine, including all vertebrate species.
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