fertsy papers
The present study validated the generalizability of our AI-based embryo assessment system across different brands of time-lapse imaging systems.
No Abstract Available
An interpretable AI model was designed to quantify Day 5 blastocyst dynamics, including growth and spontaneous collapse, improving implantation prediction compared to conventional approaches.
This prospective cohort study demonstrates that an interpretable AI model may assist embryologists in improving implantation rates during single-blastocyst transfer. The findings support the potential clinical value of AI-assisted embryo selection, warranting further validation in broader settings.
A deep learning framework was developed to assess blastocyst quality from multifocal images, with a multichannel VGG-16 model enabling accurate and objective morphological analysis.