P-237 Fertilization prediction of mature oocyte based on microscopic image of first polar body morphology using YOLOv8

Abstract

Abstract Study question Can a machine learning-based model predict successful fertilization of oocyte using first polar body morphology? Summary answer With small imbalance oocyte dataset, the most successful model to predict fertilization of mature oocyte is YOLOv8lcls with 97.47% accuracy, 100.00% specificity, and 94.87% sensitivity. What is known already The development of oocytes is crucial in the development and fertility of embryos in In Vitro Fertilization (IVF). There are several internal and external factors that could affect the fertilization competency of oocytes, one of which is the first polar body (PBI). The importance of the quality of PBI in ICSI has long been studied and has been evaluated by scoring systems regarding Istanbul Consensus. However, there is still a debate regarding the influence of PBI on the developmental competency of oocytes. Study design, size, duration This study involved the analysis of retrospective data including the microscopic light image of meiosis II (MII) oocytes at the time of Intracytoplasmic sperm injection (ICSI), along with polar body scoring by embryologists and the fertilization outcome of 1006 MII oocytes from a single public IVF clinic, between 2020-2022. After ICSI, fertilized oocytes were classified into 2 groups: 2PN and non-2PN (e.g., 0PN, 1PN, > =3PN). Participants/materials, setting, methods Light microscopic images of oocytes in the metaphase of meiosis II (MII) stage are captured, followed by the prediction of the likelihood of 2PN development after fertilization through intra-cytoplasmic sperm injection (ICSI). The image has its PBI segmented by the CNN and then cropped out of the original image. The PBI image is then fed into the prediction CNN to obtain the final result. The model performance was determined by evaluating accuracy, sensitivity, and specificity. Main results and the role of chance There were 1006 images of MII oocytes included in this study. After ICSI, 860 oocytes were fertilized resulting in the 2PN stage embryos and were classified as 2PN, whereas 146 oocytes were not fertilized or abnormally fertilized and were classified as non-2PN. Among these images, 782 oocytes received a PBI score of A, 677 of which are 2PN and 105 are non-2PN. The B grade consists of 224 images, of which are 183 2PN, and 41 non-2PN. The dataset was split into 3 subsets for training (70%), validation (10%), and testing (20%). The images are augmented by horizontal flipping to increase the amount of data and allow the model to observe several positions of the PBI. The YOLOv8 segmentation model is applied to segment the PBI and locate its position. The image is then rotated to move the PBI to the top and the PBI is cropped based on its position. Lastly, the cropped PBI image is used to predict the development by using the YOLOv8 Classify model trained on an augmented dataset. The YOLOv8l-cls model achieves the highest accuracy at 97.47%, specificity at 100.00%, and sensitivity at 94.87%. Limitations, reasons for caution The model does not include information on sperm quality, which also essentially contributes to the fertilization process. 2PN stage prediction is just the initial and surrogate outcome in IVF treatment. The model’s interpretation of the PBI score remains inconclusive. Therefore, additional experiments using dataset with balanced PBI grading are recommended. Wider implications of the findings The developed model is the first step in paving the way to establish and implement tools for assisting embryologists in selecting and prioritizing the high-potential oocyte for fertilization. Additional factors such as patient characteristics, other features of oocytes, and male factors should be further integrated to improve model performance. Trial registration number NA