AI-based assessment of embryo viability correlates with features of embryo ploidy
Presented at: ESHRE Virtual 37th Annual Meeting, Poster Presentation
Authors: M. VerMilyea1, S. Diakiw2, J. Hall2,3, M. Dakka2, T. Nguyen2, D. Perugini2, M. Perugini2. (1Ovation Fertility, Laboratory, Austin, U.S.A. 2Presagen, Life Whisperer, Adelaide, Australia. 3Australia/Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, The University of Adelaide, Adelaide, Australia.)
Study question: Do AI models used to assess embryo viability (based on pregnancy outcome) also correlate with known embryo quality measures such as ploidy status?
Summary answer: An AI for embryo viability assessment correlated with ploidy status, and with karyotypic features of aneuploidy, supporting its use for embryo selection.
What is known already: One factor that can influence pregnancy success is the genetic status of the embryo. PGT-A is commonly used to test for embryo ploidy, with the aim of identifying karyotypically normal embryos (euploid embryos), for preferential transfer. There is evidence suggesting that transfer of euploid embryos produces favorable clinical outcomes over aneuploid embryos. Given the AI model was trained to evaluate clinical pregnancy, it was hypothesized that the score might also correlate with ploidy status, and with different types of aneuploidies. Little is known about morphological correlations with embryo ploidy status, so we also sought to explore this relationship.
Study design, size, duration: This study involved analysis of a retrospective dataset of single static Day 5 embryo (blastocyst) images with associated PGT-A results and AI viability scores. The dataset comprised images of 5,469 embryos from 2,615 consecutive patients treated at five US IVF clinics between February 2015 and April 2020. The AI was trained on thousands of Day 5 embryo images from multiple IVF laboratories in multiple countries, but was not trained on data used in this study.
Participants/materials, setting, methods
Average patient age was 36.2 years, and average embryo cohort size was 2.1/patient. PGT-A analysis was performed on embryos at time of evaluation. The dataset comprised 3,251 (59.4%) euploid embryos, 1,815 (33.2%) aneuploid embryos, and 403 (7.4%) mosaic embryos. The AI was retrospectively used to provide a score between 0 (predicted non-viable) and 10 (predicted viable) for each image. Correlation between the AI viability score and euploid, mosaic and aneuploid embryos was then assessed.
Main results and the role of chance: Results showed a statistically significant correlation between AI viability score and PGT-A outcome, consistent with a relationship between pregnancy outcome and ploidy status. The average score for euploid embryos was 8.20, which was significantly higher than the average score for aneuploid embryos of 7.80 (p<0.0001).
There was a significant linear increase in confidence score from full aneuploid embryos, through mosaic embryos (average score 7.97), to full euploid embryos (mosaic threshold of 20-80%). High mosaic embryos tended to have a lower average score (7.60) than low mosaic embryos (7.96), consistent with correlation of viability (pregnancy outcome) with the degree of mosaicism. AI viability score also correlated with ploidy features believed to affect pregnancy outcomes. Trisomic changes had higher average scores than monosomic changes. Segmental changes had higher average scores than full gain or loss. The AI score differentiated euploid from aneuploid status more efficiently in embryos with poorer morphology than those with good morphology.
Whilst there was an evident correlation between pregnancy outcome and ploidy status, the AI was only weakly predictive of euploidy, with an accuracy of 57.3% using an AI viability score threshold of 7.5/10.This suggests pregnancy-related morphological features are somewhat correlated with embryo ploidy, but not completely.
Limitations, reasons for caution
The PGT-A technique is held to have some limitations for evaluating ploidy status, therefore it would be of benefit to perform additional confirmatory studies on independent datasets. It would be of interest to conduct prospective studies evaluating correlations between the AI’s evaluation of morphology and pregnancy outcome with ploidy status.
Wider implications of the findings
The AI score correlated with genetic features of embryos that are known to correlate with pregnancy, which further supports the efficacy and use of AI for embryo viability assessment. The AI identified morphological features that are somewhat predictive of ploidy status, with potential application to embryos of poorer Gardner score.