Cristina Hickman

Artificial intelligence assessment of time-lapse images can predict with 77% accuracy whether a human embryo capable of achieving a pregnancy will miscarry

José Celso Rocha, Rishabh Hariharan, Peter He, 2019, Fertility and Sterility.

Robust Automated Assessment of Human Blastocyst Quality using Deep Learning

Ehsan Kazemi, Iman Hajirasouliha, M. Toschi, 2018, bioRxiv.

Is there any room to improve embryo selection? artificial intelligence technology applied for ive birth prediction on blastocysts

José Celso Rocha, Marcos Meseguer, M. Toschi, 2019, Fertility and Sterility.

Using Artificial Intelligence to Improve the Evaluation of Human Blastocyst Morphology

José Celso Rocha, Marcelo Fábio Gouveia Nogueira, Cristina Hickman, 2017, IJCCI.

MACHINE LEARNING ALGORITHMS CAN PREDICT FUTURE BLASTOCYST DEVELOPMENT AND BLASTOCYST QUALITY BASED EMBRYOS CHARACTERISTICS ON DAY 1, 2 AND 3 WITH ACCURACY RESPECTFULLY OF 77% AND 69% BASED ON MORPHOLOGIC ASSESSMENT

Cristina Hickman, Céline Jacques, Jerome Chambost, 2021, Fertility and Sterility.

Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization

Ehsan Kazemi, M. Toschi, Nikica Zaninovic, 2019, npj Digital Medicine.