Non-invasively predicting euploidy in human blastocysts via quantitative 3D morphology measurement: a retrospective cohort study

• Main Authors: Guanqiao Shan (Author), Khaled Abdalla (Author), Hang Liu (Author), Changsheng Dai (Author), Justin Tan (Author), Junhui Law (Author), Carolyn Steinberg (Author), Ang Li (Author), Iryna Kuznyetsova (Author), Zhuoran Zhang (Author), Clifford Librach (Author), Yu Sun (Author)
• Format: Book
• Published: BMC, 2024-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12958-024-01302-x; 1477-7827
• Keywords: Blastocyst; Euploidy prediction; 3D morphology measurement; Machine learning; Gynecology and obstetrics; RG1-991; Reproduction; QH471-489
• Source: Reproductive Biology and Endocrinology, Vol 22, Iss 1, Pp 1-12 (2024)

Abstract 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.