P-235 An analysis of qualitative and quantitative morphokinetic parameters automatically annotated using CHLOE (Fairtility), an AI-based tool, finds AI score predictive of blastulation and ploidy

Abstract Study question What is the relationship between qualitative and quantitative morphokinetic parameters automatically annotated using CHLOE(Fairtility), an AI-based tool? Summary answer CHLOE score is associated with ploidy. DUC embryos have lower blastulation, form fewer good blastocysts, ha...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Human reproduction (Oxford) 2022-06, Vol.37 (Supplement_1)
Hauptverfasser: Gómez, E, Brualla-Mora, A, Almunia, N, Jiménez, R, Hickman, C, Har-vardi, I, Villaquirán, A.M
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Abstract Study question What is the relationship between qualitative and quantitative morphokinetic parameters automatically annotated using CHLOE(Fairtility), an AI-based tool? Summary answer CHLOE score is associated with ploidy. DUC embryos have lower blastulation, form fewer good blastocysts, have increased fragmentation, slower development, lower implantation than non-DUCs. What is known already The introduction of time-lapse technologies in IVF has led to the discovery of quantitiative and qualitative morphokinetic parameters which are predictive of embryo viability (ESHRE Workshop group, 2020). The challenges of annotating videos manually remain: (i)operator variation, (ii)time-consuming; (iii)complexity of how to prioritise numerous features when determining which embryos to transfer, freeze or discard. CHLOE (Fairtility) is an AI-based tool designed to automatically capture these parameters from the time-lapse videos, removing the “black box” associated with AI, and, instead, bringing transparency and support to the embryologist responsible for the decision, thus, enhancing personalisation of care down to each individual embryo. Study design, size, duration Prospective cohort analysis on time-lapse data retrospectively collected at a single private fertility clinic in Spain between 2018-2020. 693 videos were automatically annotated (without training) using the CHLOE Artificial Intelligence (AI) tool for the following quantitative features: tPNa,tPNf,t2,t3,t4,t5,t6,t7,t8,t9,tM,tsB,tB,teB, size of ICM; and the following qualitative parameters: number of pronucleates, morphological quality of Inner Cell Mass and Trophectoderm (CHLOE Morphological scoring), identification of unusual embryo cleavages i.e. Direct Uneven Cleavage (DUCs), amongst other features. Participants/materials, setting, methods All embryos were cultured using the Embryoscope (Vitrolife) incubator. Using a range of algorithms, CHLOE generated a prediction of blastulation (at 30hpi) and implantation which were compared to outcome (blastocysts vs non-blastocysts; euploids vs Aneuploids Mosaics vs euploids&aneuploids). Embryos identified as DUCS by CHLOE were compared with non-DUCs in terms of outcomes and in terms of endpoints generated by CHLOE (parametric continuous data assessed using 2-tail t-test, categorical data using chi-square). Main results and the role of chance Within all cleaved embryos analysed (n = 693), 29% were DUCs. DUC embryos were less likely to blastulate (DUCvsN
ISSN:0268-1161
1460-2350
DOI:10.1093/humrep/deac107.225