An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential.

ABSTRACT: Blastocyst selection is primarily based on morphological scoring systems and morphokinetic data. These methods involve subjective grading and time-consuming techniques. Artificial intelligence allows for objective and quick blastocyst selection. In this study, 608 blastocysts were selected for transfer using morphokinetics and Gardner criteria. Retrospectively, morphometric parameters of blastocyst size, inner cell mass (ICM) size, ICM-to-blastocyst size ratio, and ICM shape were automatically measured by a semantic segmentation neural network model. The model was trained on 1506 videos with 102 videos for validation with no overlap between the ICM and trophectoderm models. Univariable logistic analysis found blastocyst size and ICM-to-blastocyst size ratio to be significantly associated w

SUBMITTER: Fruchter-Goldmeier Y 

PROVIDER: S-EPMC10480200 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature