P-047 Sperm moving pattern analysis with artificial intelligence solution predicts aneuploidy ratios of zygotes

Abstract

Abstract Study question Can an analysis of sperm movements with artificial intelligence predict aneuploidy ratios of zygotes? Summary answer And sperm movement patterns analyzed by AI diagnosis predicted the aneuploidy ratios of zygotes. What is known already Sperm motilities present a specific pattern like strength, strength with curving, curving only and none progressive et al. Sperm movement patterns are determined by the sperm neck protein which is the centromere protein. Also, this centromere protein of the sperms was involved in the chromosome segregation in the zygote. Study design, size, duration A total of 8,000 clinical participants were recruited by CHA Infertility Center Seoul Station and 5 videos were taken from the semen sample of each participant. Machine learning with sperm moving clip for the diagnosis algorithm predicted aneuploidy ratios of fertilized embryos. Participants/materials, setting, methods We used YOLO_v8 and EfficientNet algorithm on the extracted sperm movement trajectory image to classify sperm into classes according to their motion pattern. This information was combined with the age of the patient to predict the possibility of infertility by analyzing the association with aneuploidy ratios of zygotes using deep learning model. Main results and the role of chance Deep learning AI algorithm utilized diagnosis for unexplained male infertility. No strength moving sperm pattern has shown association with male origin aneuploidy after fertilization. Especially high ratios of cycle moving sperm significantly increased the aneuploidy ratios in the zygote. But paternal aging has no correlation with aneuploidy ratios in the embryo. AI diagnosis have a 0.9 accuracy score. It is expected that the AI solution generated through this system can help overcome the problem of finding the cause of male infertility by predicting aneuploidy ratios of zygotes. Limitations, reasons for caution This study is designed for and performed with a minimum number of sperm move data. For clinical application, further mass data was set-up to increase accuracy of diagnosis. Wider implications of the findings Our AI solution was constructed to predict the possibility of aneuploidy ratios in the zygote by analyzing and classifying the movement patterns of sperm. Therefore, sperm movement-based AI diagnosis solution can be applied to define unexplained male infertility. Trial registration number non-clinical trials