Deep learning algorithms using embryo morphology can more accurately predict gestational sac (G-SAC) than fetal heartbeat possibly due to non-embryo factors

OBJECTIVE

To assess deep learning algorithms to predict implantation in in vitro fertilization while improving performance of the CNN models trained on embryo images with high variance.

MATERIALS AND METHODS

We retrospectively collected single static images of 1,741 day 5 blastocysts from 1,068 patients who underwent embryo transfer at a single in vitro fertilization (IVF) clinic between January 2015 and March 2021. The images were collected from standard optical light microscopes and matched with pregnancy data such as gestational sac (G-sac) and fetal heartbeat (FHB). We built two convolutional neural network (CNN) models with different pregnancy outcomes; G-sac and FHB, and compared the accuracy and the area under the receiver-operating curve (AUROC). We also observed high variance in visual properties such as color, brightness and contrast as the embryo images were taken under various environments. We applied the MixUp data augmentation method known to maintain the performance of models trained on datasets with high variance. The dataset was splitted into a training set and a test set with a ratio of 8:2.

RESULTS

The AUROCs of the CNN models predicting G-sac and FHB were 0.78 and 0.72, respectively. After MixUp augmentation, the AUROCs improved to 0.80 and 0.79, respectively. The accuracies of the CNN models predicting G-sac and FHB were 0.75 and 0.63. After applying Mixup, the accuracies went up to 0.75 and 0.68, respectively.

CONCLUSIONS

The CNN models built based on day 5 embryo images successfully predicted Gsac and FHB with high accuracy. Overall, the performance of the G-sac prediction model was better than that of the FHB model. It is expected that non-embryo factors like uterus and immunology become more important as pregnancy advances. Further study to include nonembryo factors in the CNN model may improve the predictive model. We also acknowledge that a high variance in images can reduce performances of the CNN models. In this study, we demonstrated that data augmentation methods like Mixup can improve the performance of the models using images in high variance. The limitation of this study is that it was a retrospective study performed on embryo image data from a single IVF clinic. Cross validation using training data from one clinic and test data from another clinic is warranted.

IMPACT STATEMENT

In this study, we demonstrated that the CNN model successfully predicted clinical pregnancy with high accuracy. However, the further pregnancy advances, the more factors influence the maintenance of pregnancy. To improve the prediction models, further studies are required to identify additional non-embryo factors to include in the models.