#319 : Blastocyst Formation Prediction Based on Deep Learning Model from 3-Day Embryo Images in Time-Lapse Incubator Using Data Augmentation

Background and Aims: Current literature suggests that blastocyst ET (Embryo Transfer) at day 5 improves pregnancy outcomes compared with cleavage ET at day 3. However, blastocyst ET poses potential challenges due to the risk of developmental arrest at the cleavage stage. Therefore, accurately predicting blastocyst formation in embryos will help determining optimal days for embryo culture. The aim of our study is to develop a deep learning-based classification model that can predict blastocyst formation based on 3-day embryo images captured from Time-Lapse incubators. Method: A total of 200 embryo images were collected at 72 hours after ICSI (Intracytoplasmic Sperm Injection), with 100 forming blastocysts and 100 failing to do so. The images were annotated by RectLabel for object detection and segmentation, and data augmentation techniques were applied to enhance the dataset. A pre-trained ResNet model was used as the basis for our classifier model, and optimization was performed using the Adam optimizer and BCEWithLogitsLoss as the loss function. Model performance was evaluated through classification accuracy and AUC score. Results: The CNN-based classifier models demonstrated a considerable accuracy of 87.5% and an AUC of 0.896 in predicting blastocyst formation, with sensitivity and specificity of 91.7% and 81.3%, respectively. It was found that without data augmentation, the model degraded to an accuracy of 62.5% and an AUC of 0.631. Conclusion: Our study competently developed a classification model for predicting blastocyst formation using 3-day embryo images from a Time-Lapse incubator and demonstrated the effectiveness of data augmentation techniques in limited data sets. This deep learning-based model may contribute as a beneficial tool in IVF (In Vitro Fertilization) process.