Automatic Ploidy Prediction and Quality Assessment of Human Blastocyst Using Time-Lapse Imaging
Suraj Rajendran, Matthew Brendel, Josue Barnes, Qiansheng Zhan, Jonas E. Malmsten, Pantelis Zisimopoulos, Alexandros Sigaras, Kwabena Ofori-Atta, Marcos Meseguer, Kathleen A. Miller, David I. Hoffman, Zev Rosenwaks, Olivier Elemento, Nikica Zaninović, Iman Hajirasouliha
This paper develops BELA, a deep learning model for ploidy prediction in human blastocysts using time-lapse imaging, achieving an AUC of 0.76 on the Weill Cornell dataset.
Nature Communications · Sep 5, 2024Peer-reviewedDevelopmental Biology