» Articles » PMID: 37300648

Delineating the Heterogeneity of Embryo Preimplantation Development Using Automated and Accurate Morphokinetic Annotation

Overview
Publisher Springer
Date 2023 Jun 10
PMID 37300648
Authors
Affiliations
Soon will be listed here.
Abstract

Purpose: Our objective was to design an automated deep learning model that extracts the morphokinetic events of embryos that were recorded by time-lapse incubators. Using automated annotation, we set out to characterize the temporal heterogeneity of preimplantation development across a large number of embryos.

Methods: To perform a retrospective study, we used a dataset of video files of 67,707 embryos from four IVF clinics. A convolutional neural network (CNN) model was trained to assess the developmental states that appear in single frames from 20,253 manually-annotated embryos. Probability-weighted superposition of multiple predicted states was permitted, thus accounting for visual uncertainties. Superimposed embryo states were collapsed onto discrete series of morphokinetic events via monotonic regression of whole-embryo profiles. Unsupervised K-means clustering was applied to define subpopulations of embryos of distinctive morphokinetic profiles.

Results: We perform automated assessment of single-frame embryo states with 97% accuracy and demonstrate whole-embryo morphokinetic annotation with R-square 0.994. High quality embryos that had been valid candidates for transfer were clustered into nine subpopulations, as characterized by distinctive developmental dynamics. Retrospective comparative analysis of transfer versus implantation rates reveals differences between embryo clusters as marked by poor synchronization of the third mitotic cell-cleavage cycle.

Conclusions: By demonstrating fully automated, accurate, and standardized morphokinetic annotation of time-lapse embryo recordings from IVF clinics, we provide practical means to overcome current limitations that hinder the implementation of morphokinetic decision-support tools within clinical IVF settings due to inter-observer and intra-observer manual annotation variations and workload constrains. Furthermore, our work provides a platform to address embryo heterogeneity using dimensionality-reduced morphokinetic descriptions of preimplantation development.

Citing Articles

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems.

Canat G, Duval A, Gidel-Dissler N, Boussommier-Calleja A Sci Rep. 2024; 14(1):29016.

PMID: 39578525 PMC: 11584792. DOI: 10.1038/s41598-024-80565-1.


Evaluating the heterogeneous effect of extended culture to blastocyst transfer on the implantation outcome via causal inference in fresh ICSI cycles.

Kan-Tor Y, Srebnik N, Gavish M, Shalit U, Buxboim A J Assist Reprod Genet. 2024; 41(3):703-715.

PMID: 38321264 PMC: 10957840. DOI: 10.1007/s10815-024-03023-x.


Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine.

Luong T, Le N J Assist Reprod Genet. 2023; 41(2):239-252.

PMID: 37880512 PMC: 10894798. DOI: 10.1007/s10815-023-02973-y.


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

Fruchter-Goldmeier Y, Kantor B, Ben-Meir A, Wainstock T, Erlich I, Levitas E Sci Rep. 2023; 13(1):14617.

PMID: 37669976 PMC: 10480200. DOI: 10.1038/s41598-023-40923-x.


Embryo classification beyond pregnancy: early prediction of first trimester miscarriage using machine learning.

Amitai T, Kan-Tor Y, Or Y, Shoham Z, Shofaro Y, Richter D J Assist Reprod Genet. 2022; 40(2):309-322.

PMID: 36194342 PMC: 9935804. DOI: 10.1007/s10815-022-02619-5.

References
1.
Blank C, Wildeboer R, DeCroo I, Tilleman K, Weyers B, De Sutter P . Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective. Fertil Steril. 2019; 111(2):318-326. DOI: 10.1016/j.fertnstert.2018.10.030. View

2.
Gardner D, Meseguer M, Rubio C, Treff N . Diagnosis of human preimplantation embryo viability. Hum Reprod Update. 2015; 21(6):727-47. DOI: 10.1093/humupd/dmu064. View

3.
Gardner D, Lane M, Stevens J, Schoolcraft W . Noninvasive assessment of human embryo nutrient consumption as a measure of developmental potential. Fertil Steril. 2001; 76(6):1175-80. DOI: 10.1016/s0015-0282(01)02888-6. View

4.
Wong C, Loewke K, Bossert N, Behr B, De Jonge C, Baer T . Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat Biotechnol. 2010; 28(10):1115-21. DOI: 10.1038/nbt.1686. View

5.
Chamayou S, Patrizio P, Storaci G, Tomaselli V, Alecci C, Ragolia C . The use of morphokinetic parameters to select all embryos with full capacity to implant. J Assist Reprod Genet. 2013; 30(5):703-10. PMC: 3663978. DOI: 10.1007/s10815-013-9992-2. View