P-227 Blastocyst rate prediction by artificial intelligence: evaluation of static oocyte images in patients and donors
Authors: Viloria Samochin T  A , Murria L , Rebeca G  P , Laura C  S , Marcos M 
Published: July 2024

Study question

Can a deep learning tool accurately predict blastocyst development from single static oocyte images across diverse patient populations?

Summary answer

The automatic oocyte score was directly correlated with the blastocyst formation rate in conventional treatments using patient oocytes and in the egg donation program.

What is known already

The implementation of artificial intelligence (AI)-based algorithms has revolutionized gamete and embryo evaluation in assisted reproduction, providing a standardized basis for clinical decisions. Despite these advances, the implementation of AI algorithms in oocyte evaluation has faced unique challenges. This study explores the predictive ability of an AI algorithm on the actual blastocyst arrival rate from static oocyte images, focusing on finding differences or similarities in these predictions between patients and donors.

Study design, size, duration

This retrospective cohort study analyzed 1092 static images of oocytes before ICSI. The sample included 791 donor and 301 patient oocyte images. A deep learning algorithm was employed to assess oocyte scores, categorizing them into quartiles. The study explored the correlation between oocyte scores and the actual rate of blastocyst arrival for all oocytes. The rates were also compared between patients and donors, assessing an effective cut-off point to improve the accuracy of the predictions.

Participants/materials, setting, methods

The oocyte images analyzed were captured with cameras connected to microinjection microscopes immediately before the ICSI procedure. Oocyte images and oocyte age were used as input data for the blind analysis performed by the deep learning-based algorithm. The algorithm predicted blastocyst arrival probability for each oocyte, and these predictions were compared with actual outcomes. This evaluation assessed the algorithm’s performance across patient and donor populations, providing valuable insights into its predictive accuracy for blastocyst arrival.

Main results and the role of chance

Significant differences in blastocyst formation rates were observed between quartiles of scores assigned by the artificial intelligence (AI) algorithm. Oocytes classified in Quartile 1 showed a significantly lower blastocyst arrival rate (54.63±49.99, n = 216) compared to Quartiles 3 (71.88±45.06, n = 224) and 4 (66.82±47.19, n = 220)*. In general, each increased unit in the algorithm was significantly associated with an increased probability of blastocyst formation (OR = 1.10 [95% CI: 1.02-1.16])*. The algorithm successfully predicted the blastocyst arrival rate, with an area under the curve (AUC) of 0.60 (95% CI: 0.56-0.65). When assessing differences between patients and donors, both groups exhibited similar rates in the first quartile (59.68% and 52.60%, respectively) and the three last quartiles combined (67.55% and 66.02%, respectively). However, an effective cutoff point was identified at 2.8 (between first and second quartile), where both populations experienced a notable increase in blastocyst formation rates. (*p&lt;.05)

Limitations, reasons for caution

This project is limited by its retrospective, single-center nature and small sample size. Consequently, it would be advisable to conduct a large-scale multicenter study to confirm the conclusions of this study.

Wider implications of the findings

This study highlights the efficiency of the artificial intelligence algorithm in predicting blastocyst arrival from oocyte images. Establishing a cutoff point in oocyte scoring could enhance the management of the egg donation program and provide individualized guidance to patients based on the quality of their gametes.

Trial registration number

not applicable