Literature DB >> 33719832

A machine learning system with reinforcement capacity for predicting the fate of an ART embryo.

Sandrine Giscard d'Estaing1,2,3, Elsa Labrune1,2,4, Maxence Forcellini5, Cecile Edel1,3, Bruno Salle1,2,3, Jacqueline Lornage1,2,3, Mehdi Benchaib1,2,4,5.   

Abstract

The aim of this work was to construct a score issued from a machine learning system with self-improvement capacity able to predict the fate of an ART embryo incubated in a time lapse monitoring (TLM) system. A retrospective study was performed. For the training data group, 110 couples were included and, 891 embryos were cultured. For the global setting data group, 201 couples were included, and 1186 embryos were cultured. No image analysis was used; morphokinetic parameters from the first three days of embryo culture were used to perform a logistic regression between the cell number and time. A score named DynScore was constructed, the prediction power of the DynScore on blastocyst formation and the baby delivery were tested via the area under the curve (AUC) obtained from the receiver operating characteristic (ROC). In the training data group, the DynScore allowed the blastocyst formation prediction (AUC = 0.634, p < 0.001), this approach was the higher among the set of the tested scores. Similar results were found with the global setting data group (AUC = 0.638, p < 0.001) and it was possible to increase the AUC of the DynScore with a regular update of the prediction system by reinforcement, with an AUC able to reach a value above 0.9. As only the best blastocysts were transferred, none of the tested scores was able to predict delivery. In conclusion, the DynScore seems to be able to predict the fate of an embryo. The reinforcement of the prediction system allows maintaining the predictive capacity of DynScore irrespective of the various events that may occur during the ART process. The DynScore could be implemented in any TLM system and adapted by itself to the data of any ART center.Abbreviations: ART: assisted reproduction technology; TLM: time lapse monitoring system; AUC: area under the curve; ROC: receiver operating characteristic; eSET: elective single embryo transfer; AIS: artificial intelligence system; KID: known implantation data; AMH: anti-Müllerian hormone; BMI: body mass index; WHO: World Health Organization; c-IVF: conventional in-vitro fertilization; ICSI: intracytoplasmic sperm injection; PNf: pronuclear formation; D3: day 3; D5: day 5; D6: day 6; GnRH: gonadotrophin releasing hormone; FSH: follicle stimulating hormone; LH: luteinizing hormone; hCG: human chorionic gonadotropin; PVP: polyvinyl pyrrolidone; PNf: time of pronuclear fading; tx: time of cleavage to x blastomeres embryo; ICM: inner cell mass; TE: trophectoderm; NbCellt: number of cells at t time; FIFO: first in first out; TD: training data group; SD: setting data group; R: real world.

Entities:  

Keywords:  ART procedure; blastocyst formation; morphokinetics; time lapse monitoring system

Year:  2021        PMID: 33719832     DOI: 10.1080/19396368.2020.1822953

Source DB:  PubMed          Journal:  Syst Biol Reprod Med        ISSN: 1939-6368            Impact factor:   3.061


  2 in total

1.  Development of a dynamic machine learning algorithm to predict clinical pregnancy and live birth rate with embryo morphokinetics.

Authors:  Liubin Yang; Mary Peavey; Khalied Kaskar; Neil Chappell; Lynn Zhu; Darius Devlin; Cecilia Valdes; Amy Schutt; Terri Woodard; Paul Zarutskie; Richard Cochran; William E Gibbons
Journal:  F S Rep       Date:  2022-04-15

2.  Does conventional morphological evaluation still play a role in predicting blastocyst formation?

Authors:  Xiaoming Jiang; Jiali Cai; Lanlan Liu; Zhenfang Liu; Wenjie Wang; Jinhua Chen; Chao Yang; Jie Geng; Caihui Ma; Jianzhi Ren
Journal:  Reprod Biol Endocrinol       Date:  2022-04-19       Impact factor: 4.982

  2 in total

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