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

Yael Fruchter-Goldmeier¹, Ben Kantor², Assaf Ben-Meir^2,3, Tamar Wainstock⁴, Itay Erlich², Eliahu Levitas^1,5, Yoel Shufaro^6,7, Onit Sapir^6,7, Iris Har-Vardi^8,9,10

¹The Medical School for International Health and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

²Fairtility Ltd., Tel Aviv, Israel.

³Fertility and IVF Unit, Department of Obstetrics and Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.

⁴School of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

⁵Fertility and IVF Unit, Department of Obstetrics and Gynecology, Soroka University Medical Center, Beer-Sheva, Israel.

⁶Infertility and IVF Unit, Beilinson Women's Hospital, Rabin Medical Center, Petach-Tikva, Israel.

⁷The Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

⁸The Medical School for International Health and the Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. harvardi@bgu.ac.il.

⁹Fairtility Ltd., Tel Aviv, Israel. harvardi@bgu.ac.il.

¹⁰Fertility and IVF Unit, Department of Obstetrics and Gynecology, Soroka University Medical Center, Beer-Sheva, Israel. harvardi@bgu.ac.il.

PubMed PMC DOI PDF

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 blastocys…

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