Revisiting morphokinetic analysis: Are discrete temporal events predictive of embryo developmental potential?

Yoav Kan-Tor 1 Ity Erlich 1 Tamar Amitai 1 Einat Eisenman 2 Yuval Or 3 Zeev Shoham 3 Miriam Almagor 3 Yoel Shufaro 4 Benjamin Fisch 4 Onit Sapir 4 Arieh Horowitz 2 Matan Gavish 1 Amnon Buxboim 1 Assaf Ben-Meir 2
1The Alexender Grass Center for Bioengineering, School of computer Science and Engineering, Hebrew University
2IVF Unit Department of Obstetrics and Gynecology, Hebrew University Hadassah Medical Center
3Infertility and IVF Unit, Kaplan Medical Center, Hebrew University
4Infertility and IVF Unit, Beilinson Women's Hospital, Rabin Medical Center, Sackler Faculty of Medicine, Tel Aviv University

Introduction: Current methods fail to evaluate the developmental competence of pre-implanted embryos. As a result, the practice is to transfer more than one embryo to obtain reasonable pregnancy rates. The prediction of embryogenic developmental competence at early stages of pre-implantation development will enable identifying the highest quality embryos for transfer, thus significantly increase live-birth rates and reduce time-to-pregnancy and multiple pregnancy. The current state of the art in IVF clinics is based on time-lapse incubators with annotating the morphokinetics events to use an algorithm to predict the embryo potential (like KIDScoreTM). Unfortunately, this is a non-standardized and time consuming process with limited reproducible predictive power.

Aim: Revisiting morphokinetic annotation schemes to define the potential predictive power of the first three days of preimplantation development.

Material & Methods: Clinical data (age, ET results) and embryonic development videos were collected from four IVF units. We utilize three state-of-the-art unbiased statistical and machine learning models to define the information contained in the video and clinical data: Monotonic regression, Logistic regression and convolutional neural network (CNN).

Results: Using a large database, we analyze the first three days of preimplantation development. We exclude embryos that failed to reach the four-cell stage by day-3. The analysis of transferred and non-transferred embryos showed limited performances in discriminating the high-performing from the low-performing embryos. All the models utilized here share these results.

Conclusions: We define the limited capacity of morphological and morphokinetic analyses in identifying high-potential embryos by day-3 of preimplantation development. Our results motivate analyses of data that extend beyond the discrete description of morphokinetic events and are based on raw time-lapse images using machine-learning methods.

Assaf Ben-Meir
Assaf Ben-Meir
TCART








Powered by Eventact EMS