O-158 Prediction of implantation of vitrified-warmed blastocysts using a deep learning algorithm on a single post-warming image of each embryo
Authors: Conversa Martínez L , Valera Cerdá M Á , Bori Arnal L , Murria Aguilar L , Cobo Cabal A , Meseguer Escrivá M 
Published: June 2023

Study question

Can a deep learning algorithm determine the implantation likelihood of vitrified blastocysts from single time-point post-warming time-lapse images obtained from EmbryoScope?

Summary answer

A significant positive correlation was found between the score given by the deep learning algorithm and the implantation rate of the frozen blastocysts analyzed.

What is known already

Despite the safety and success of the vitrification technique, many of the best morphologically classified embryos prior to vitrification do not maintain their characteristics when returned to room temperature. Since artificial intelligence (AI) has been shown to be useful in assessing the quality of fresh embryos at different stages, it could also be used to predict the outcome of frozen embryo transfers. This is the first attempt to distinguish vitrified blastocysts that will implant from those that will not by applying deep learning on a single image taken in the period between warming and transfer.

Study design, size, duration

This retrospective single-centre study included 689 blastocysts with known implantation data. All of them were vitrified and warmed using the Cryotop method (Kitazato, Biopharma, Japan). Warmed embryos were assessed by experienced embryologists according to the ASEBIR morphological scoring system and the degree of expansion. Immediately after warming, blastocysts were placed in EmbryoScope (Vitrolife, Denmark) time-lapse incubators for 2-5 hours to let them re-expand until embryo transfer.

Participants/materials, setting, methods

A pre-transfer embryo image was provided to the algorithm (Life Whisperer Viability), which returned scores from 0 to 10. Data were analyzed by chi-square test or one-way ANOVA. Multivariate logistic regression analysis was performed including embryo score, oocyte origin (donated vs. autologous), oocyte age, patient age, oocyte handling (fresh vs. vitrified eggs), and day of vitrification (5 vs. 6). The area under the receiver operating characteristic (ROC) curve (AUC) was used to calculate evaluation performance.

Main results and the role of chance

There was a significant difference between the scores given to vitrified implanted and non-implanted embryos. The mean value was 4.98 [95% CI 4.70-5.26]* for non-implanted embryos and 6.16 [95% CI 5.84-6.48]* for implanted embryos. When dividing the embryo score by quartiles, the implantation rate for each quartile was 27.7% for Q1, 33.7% for Q2 (Odds ratio (OR)=1.31 [0.82–2.09]), 44.8% for Q3 (OR = 2.05 [1.30–3.22]*) and 48.3% for Q4 (OR = 2.46 [1.56–3.87]*). The contribution of each increased unit of the algorithm score to the implantation (OR = 1.16 [95% CI: 1.09–1.23]*) was statistically significant. The deep learning algorithm score successfully predicted implantation with an AUC of 0.65 [95% CI: 0.60–0.69]*. Moreover, the algorithm performed better than the ASEBIR morphological classification, which achieved an AUC of 0,62 [95% CI: 0.58–0.66] in predicting implantation. *P &lt; 0.001.

Limitations, reasons for caution

Our clinic was not involved in the development of the algorithm. Furthermore, it was designed to assess fresh embryos prior to vitrification but not thawed ones, so this study should be considered an external trial. A post-warming full video analysis by AI could possibly provide additional information.

Wider implications of the findings

The use of predictive models on vitrified cycles may help to select the best frozen embryo for transfer and predict which embryos will result in implantation failure early enough to thaw another one with a better chance of success. It also indicates that AI can generalize to thawed embryo assessment.

Trial registration number

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