AI-Based Framework for Automated Cell Cleavage Detection and Timing in Embryo Time-Lapse Videos

In vitro fertilization (IVF) has become a primary therapeutic intervention for couples worldwide addressing in-fertility challenges. IVF success depends critically on embryo quality assessment, where cell cleavage timing serves as a key developmental parameter. Traditional morphological evaluation methods suffer from inter-observer variability and laborintensive manual analysis. This study presents an automated AI-based framework for cleavage stage detection and cleavage onset timing estimation from Time-Lapse Microscopy (TLM) videos to assist embryologists in embryo selection. The proposed YOLO-based approach addresses significant class imbalance through selective data augmentation and random undersampling strategies. To ensure precise temporal data, an OCR (Optical Character Recognition) library was integrated to automatically read and record the Hours Post-Insemination (HPI) timestamps from the video frames. The proposed framework accurately identifies cell division stages up to the seven-cell stage with 1-2 hours mean timing delay post-insemination. The framework achieves an overall Accuracy of 86.61% , F1-score of 86.24% ,and precision of 86.24% in cleavage stage classification, demonstrating significant improvements over existing methods, particularly in intermediate and later stages (4-cell to 8-cell transitions) where previous research have demonstrated challenges in accurately detecting them. Automated extraction of morphokinetic parameters enables objective embryo assessment, reducing subjectivity in clinical decision-making. The proposed framework demonstrated significant improvements over previous research, which frequently has trouble accurately classifying beyond early cleavage stages. This has implications in improving the selection of good-quality embryos, and thus to help improve the success rate of IVF. This work contributes to advancing assisted reproductive technology by providing reliable, automated embryo quality assessment tools.

@article{Alharbi2025,
  title     = {AI-Based Framework for Automated Cell Cleavage Detection and Timing in Embryo Time-Lapse Videos},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yasmin Alharbi and Sultanah Alshammari and Aisha Elaimi},
  doi       = {10.14569/IJACSA.2025.01612123},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612123}
}