P-279 Comparison of AI model performance in predicting blastulation in old and young mice groups
Authors: Lee H , Yoon J , Juhn K M , Yoon S H , Lim J H , Ko T , Kim H M 
Published: June 2023

Abstract

Study question
Is there a difference in AI performance in predicting blastulation between young and old mice?

Summary answer
AI performance in predicting blastulation is higher in young mice than in old mice.

What is known already
Previous studies have demonstrated AI's ability to predict oocyte development using oocyte or embryo images, but there has been no study to determine if AI performance varies with the age of the female. Maternal age influences oocyte quality and development, but other factors such as diet and environment also play a role. Using mouse experiments can minimize these confounding factors. The study aims to determine if there is a difference in AI performance for predicting blastulation using oocyte images from old and young mice.

Study design, size, duration
We collected oocyte images from 673 B6D2F1 (BDF1) mice at a single research center between July 2022 and January 2023. The samples were separated into two groups based on maternal age, 386 from young mice and 287 from old mice. The development stages including fertilization and blastulation were recorded through daily observation, and the data was divided into a training set of 3 batches and a test set of 2 batches.

Participants/materials, setting, methods
The mice were divided into two groups: young mice (7-9 weeks old) and old mice (62-68 weeks old). The study used images of MII oocytes collected after superovulation, taken with a 200x inverted microscope, and cultured in a single drop. A CNN was trained to predict blastulation and compared for accuracy between the young and old mouse groups. Blastulation was determined by observing the presence of expanded blastocysts or above on day 4.

Main results and the role of chance
The study used 203 and 182 oocyte images of young and old mice respectively for training, and 183 and 105 images for testing. Three convolutional neural network models (VGG16, ResNet50, and DenseNet121) were used to predict blastulation, and ResNet50 was selected as the final model. Without considering age, the accuracy, sensitivity, and specificity of the AI for predicting blastulation were 0.75, 0.74, and 0.76, respectively. The AI model used in the study had accuracy of 0.77, sensitivity of 0.75, and specificity of 0.79 in the young mice group. In the old mice group, the model had accuracy of 0.72, sensitivity of 0.70, and specificity of 0.74. When a t-test was performed, there was a statistically significant difference in accuracy (p = 0.014) with better accuracy in young mice. On the contrary, the sensitivity in the old mice group was the lowest, making it difficult to predict blastulation. As oocyte morphology does not always represent aneuploidy, the AI model using oocyte images may not perform well in the old mice group due to increased aneuploidy.

Limitations, reasons for caution
The limitations of this study include its retrospective design using oocyte images from only one research center. A larger, multicenter study would be beneficial.

Wider implications of the findings
The study found that AI accurately predicted blastulation using mice oocyte images, with higher accuracy for both positive and negative outcomes in young mice. However, accuracy was lower in old mice, particularly for positive outcome, suggesting the need for additional tests like genetic testing for predicting blastulation in old mice.

Trial registration number
not applicable