Development of a Non-Invasive Artificial Intelligence Algorithm for Identification of Euploid Embryos With High Morphological Quality During IVF

Presented at: ASRM 2023 Scientific Congress & Expo, October 14-18, 2023, New Orleans, Louisiana

Authors: M. D. VerMilyea, S. M. Diakiw, J . M. M. Hall, K. A. Miller, C. Giménez, L. Conversa, M. Meseguer, T. V. Nguyen, D. Perugini, K. Devine, M. Perugini

Objective: To develop an image-based artificial intelligence (AI) algorithm for combined morphological and genetic assessment of embryo quality.

Materials and Methods: Two AI algorithms, one trained on images of embryos with pre-implantation genetic testing for aneuploidies (PGT-A) outcomes (genetics AI)1, and another trained on images of embryos with pregnancy outcomes (viability AI)2, were combined to create a single AI score to assess embryo quality (EQ score). The optimum ratio of genetics AI score to viability AI score for identifying embryos that were both euploid and of high morphological quality was 2.4:1.

The EQ score was assessed for its ability to identify embryos of 3 Gardner-based quality levels: ≥ expansion grade 3 euploid embryos, ≥ 3BB euploid embryos, and ≥ 3AA euploid embryos. Performance was evaluated on a blind test set of 1474 embryo images using ROC-AUC and simulated cohort ranking3. The test set was balanced for morphology as follows: 25% ≥ 3AA, 40% ≥ 3BB, 75% ≥ expansion Grade 3, 25% expansion grades 1-2. Two independent blind test sets of 943 and 664 embryos were used to validate EQ performance based on Gardner and ASEBIR grading, respectively (not balanced). Finally, the EQ score was compared to genetics and viability AI scores alone for its ability to identify euploid embryos (test set of 936 embryo images) and pregnancy outcomes (test set of 479 embryo images), respectively.

Results: The EQ score demonstrated high predictive ability for identifying ≥ expansion grade 3 euploid embryos, ≥ 3BB euploid embryos, and ≥ 3AA euploid embryos on 2 datasets, with ROC-AUC values up to 0.772, 0.814, and 0.921, respectively. The EQ score was also able to predict embryo quality according to ASEBIR grading, with ROC-AUC values of 0.716 for ≥ Grade B euploid embryos and 0.814 for ≥ Grade A euploid embryos.

Accuracy on the balanced test set was 73-74% for each quality level. Ranking analyses showed the probability of selecting a good quality embryo as the top one in each cohort was 58%, 66%, and 76% for ≥ 3AA euploid, ≥ 3BB euploid, and ≥ expansion grade 3 euploid embryos, respectively. This increased to >95% in each case for the probability of identifying at least 1 good quality embryo in the top-3 ranked embryos in each cohort.

The EQ score outperformed both genetics and viability AI scores alone for identification of good quality embryos. It showed a similar probability of selecting a euploid embryo to the genetics AI score alone (83% versus 81%, respectively), and was at least as good at identifying embryos that led to a pregnancy as the viability AI score alone (7% reduction in transfers relative to Gardner-based ranking versus 5.8%, respectively).

Conclusions: The EQ score is highly predictive for identifying euploid embryos with high morphological quality. The combined score was comparable to individual genetics and viability AI scores for predicting PGT-A and pregnancy outcomes, respectively.

Impact Statement: High quality embryos are known to result in higher implantation rates, reduced miscarriages, and improved live birth outcomes. A one-step, non-invasive AI for identifying embryos that are both euploid and of high morphological quality will likely lead to improved IVF outcomes for patients.

References:

1. VerMilyea M, Hall JMM, Diakiw SM, Johnston A, Nguyen T, et al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Hum Reprod. 2020; 35(4): 770-84.

2. Diakiw SM, Hall JMM, VerMilyea MD, Amin J, Aizpurua J, et al. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Hum Reprod. 2022b; 37(8): 1746-59.

3. Diakiw SM, Hall JMM, VerMilyea M, Lim AYX, Quangkananurug W, et al. An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos. Reprod Biomed Online. 2022a; 45(6): 1105-17.