O-086 End-to-end deep learning for recognition of ploidy status using time-lapse videos

Abstract

Abstract Study question Our Retrospective study is to investigate an end-to-end deep learning model in identifying ploidy status through raw time-lapse video. Summary answer Our deep learning model demonstrates a proof of concept and potential in recognizing the ploidy status. What is known already Since the time-lapse system has been introduced into the IVF lab, the relationship between morphogenetic and ploidy status has been often discussed. However, the result has not yet reached a united conclusion due to some limitations such as human labeling. Besides the statistical approach, deep learning models have been utilized for ploidy prediction. As such approaches are single image-based, the performance remains unpromising as previous statistical-based research. Therefore, in order to move further toward clinical application, better research design and approach are needed. Study design, size, duration A retrospective analysis of the time-lapse videos and chromosomal status from 690 biopsied blastocysts cultured in a time-lapse incubator (EmbryoScope+, Vitrolife) between January 2017 and August 2018 in the Lee Women’s Hospital were assessed. The ploidy status of the blastocyst was derived from the PGT-A using high-resolution next-generation sequencing (hr-NGS). Embryo videos were obtained after normal fertilization through the intracytoplasmic sperm injection or conventional insemination. Participants/materials, setting, methods By randomly dividing the data into 80% and 20%, we developed our deep learning model based on Two-Stream Inflated 3D ConvNets(I3D) network. This model was trained by the 80% time-lapse videos and the PGT-A result. The remaining 20% has been tested by feeding the time-lapse video as input and the PGT-A prediction as output. Ploidy status was classified as Group 1 (aneuploidy) and Group 2 (euploidy and mosaicism). Main results and the role of chance Time-lapse videos were divided into 3-time partitions: day 1, day 1 to 3, and day 1 to 5. Deep learning models have been fed by RGB and optical flow. Combining 3 different time partitions with RGB, optical flow, and fused result from RGB and optical flow, we received nine sets of test results. According to the results, the longest time partition with the fusion method has the highest AUC result as 0.74, which appeared higher than the other eight experimental settings with a maximum increase of 0.17. Limitations, reasons for caution The present study is retrospective and future prospective research would help us to identify more key factors and improve this model. In addition, expanding sample size combined with cross-centered validation will also be considered in our future approach. Wider implications of the findings Group 1 and Group 2 approach provided deselection of aneuploidy embryos, while future deep learning approaches toward high mosaicism, low mosaicism, and euploidy will be needed, in order to provide a better clinical application. Trial registration number CS18082