Deep learning methods to forecasting human embryo development in time-lapse videos

Background In assisted reproductive technology, evaluating the quality of the embryo is crucial when selecting the most viable embryo for transferring to a woman. Assessment also plays an important role in determining the optimal transfer time, either in the cleavage stage or in the blastocyst stage. Several AI-based tools exist to automate the assessment process. However, none of the existing tools predicts upcoming video frames to assist embryologists in the early assessment of embryos. In this paper, we propose an AI system to forecast the dynamics of embryo morphology over a time period in the future. Methods The AI system is designed to analyze embryo development in the past two hours and predict the morphological changes of the embryo for the next two hours. It utilizes a predictive model incorporating Convolutional LSTM layers, to predict the future video frame by analyzing prior morphological changes within the embryo’s video sequence. The system uses the predictions recursively and forecasts up to 23 hours of embryo development. Results The results demonstrated that the AI system could accurately forecast embryo development at the cleavage stage on day 2 and the blastocyst stage on day 4. The system provided valuable information on the cell division processes on day 2 and the start of the blastocyst stage on day 4. The system focused on specific developmental features effective across both the categories of embryos. The embryos that were transferred to the female, and the embryos that were discarded. However, in the ‘transfer’ category, the forecast had a clearer cell membrane and less distortion as compared to the ‘avoid’ category. Conclusion This study assists in the embryo evaluation process by providing early insights into the quality of the embryo for both the transfer and avoid categories of videos. The embryologists recognize the ability of the forecast to depict the morphological changes of the embryo. Additionally, enhancement in image quality has the potential to make this approach relevant in clinical settings. Author summary The emergence of assisted reproductive technology has significantly improved infertility treatments. It involves fertilization of an egg outside the body, and the resultant embryos are developed in time-lapse incubators. The embryologists manually evaluate embryos using time-lapse videos and rank each embryo on the basis of several criteria including the dynamics of embryo cell stages, such as the start of the blastocyst stage. Traditional manual analysis is subjective and time-consuming, and AI tools are introduced to automate and enhance embryo selection efficiency. However, current AI tools do not generate video frames that forecast changes in embryo morphology. This study fills this gap by introducing an AI system that forecasts upcoming frames of a time-lapse video. In this approach, several hours were predicted ahead of the last video frame. The system was evaluated on crucial days of embryo evaluation. Our approach was effective in both good quality (transfer) and poor quality (avoid) video categories, and the forecast revealed crucial insights about embryo cell division and the start of the blastocyst stage. Despite some image quality issues, the proposed AI system demonstrated the potential for early and accurate assessment of embryo quality.