Literature DB >> 30054845

Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence.

Mara Simopoulou1,2, Konstantinos Sfakianoudis3, Evangelos Maziotis4, Nikolaos Antoniou4, Anna Rapani4, George Anifandis5, Panagiotis Bakas6, Stamatis Bolaris7, Agni Pantou3, Konstantinos Pantos3, Michael Koutsilieris4.   

Abstract

Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rates remains the focus of the in vitro fertilization scientific community and the strongest driver behind innovative approaches. This scoping review aims to describe and analyze complex models based on mathematics for embryo selection, devices, and software most widely employed in the IVF laboratory and algorithms in the service of the cutting-edge technology of artificial intelligence. Despite their promising nature, the practicing embryologist is the one ultimately responsible for the success of the IVF laboratory and thus the one to approve embracing pioneering technologies in routine practice. Applied mathematics and computational biology have already provided significant insight into the selection of the most competent preimplantation embryo. This review describes the leap of evolution from basic mathematics to bioinformatics and investigates the possibility that computational applications may be the means to foretell a promising future for the IVF clinical practice.

Entities:  

Keywords:  Artificial intelligence IVF; Bayesian models IVF; Devices IVF; Prediction models IVF; Time-lapse IVF

Mesh:

Year:  2018        PMID: 30054845      PMCID: PMC6133811          DOI: 10.1007/s10815-018-1266-6

Source DB:  PubMed          Journal:  J Assist Reprod Genet        ISSN: 1058-0468            Impact factor:   3.412


  60 in total

Review 1.  What is Bayesian statistics?

Authors:  Sean R Eddy
Journal:  Nat Biotechnol       Date:  2004-09       Impact factor: 54.908

2.  Timing of cell division in human cleavage-stage embryos is linked with blastocyst formation and quality.

Authors:  María Cruz; Nicolás Garrido; Javier Herrero; Inmaculada Pérez-Cano; Manuel Muñoz; Marcos Meseguer
Journal:  Reprod Biomed Online       Date:  2012-07-07       Impact factor: 3.828

3.  Time-lapse algorithms and morphological selection of day-5 embryos for transfer: a preclinical validation study.

Authors:  Ashleigh Storr; Christos Venetis; Simon Cooke; Suha Kilani; William Ledger
Journal:  Fertil Steril       Date:  2018-01-11       Impact factor: 7.329

4.  Automatic time-lapse instrument is superior to single-point morphology observation for selecting viable embryos: retrospective study in oocyte donation.

Authors:  Belén Aparicio-Ruiz; Natalia Basile; Sonia Pérez Albalá; Fernando Bronet; José Remohí; Marcos Meseguer
Journal:  Fertil Steril       Date:  2016-08-13       Impact factor: 7.329

5.  Semi-automated morphometric analysis of human embryos can reveal correlations between total embryo volume and clinical pregnancy.

Authors:  G Paternot; S Debrock; D De Neubourg; T M D'Hooghe; C Spiessens
Journal:  Hum Reprod       Date:  2013-01-12       Impact factor: 6.918

6.  Increasing the probability of selecting chromosomally normal embryos by time-lapse morphokinetics analysis.

Authors:  Natalia Basile; Maria del Carmen Nogales; Fernando Bronet; Mireia Florensa; Marissa Riqueiros; Lorena Rodrigo; Juan García-Velasco; Marcos Meseguer
Journal:  Fertil Steril       Date:  2014-01-11       Impact factor: 7.329

7.  Retrospective analysis of outcomes after IVF using an aneuploidy risk model derived from time-lapse imaging without PGS.

Authors:  Alison Campbell; Simon Fishel; Natalie Bowman; Samantha Duffy; Mark Sedler; Simon Thornton
Journal:  Reprod Biomed Online       Date:  2013-05-09       Impact factor: 3.828

8.  Computer-controlled, multilevel, morphometric analysis of blastomere size as biomarker of fragmentation and multinuclearity in human embryos.

Authors:  Christina Hnida; Elisabete Engenheiro; Søren Ziebe
Journal:  Hum Reprod       Date:  2004-02       Impact factor: 6.918

9.  Objective way to support embryo transfer: a probabilistic decision.

Authors:  L Gianaroli; M C Magli; L Gambardella; A Giusti; C Grugnetti; G Corani
Journal:  Hum Reprod       Date:  2013-03-05       Impact factor: 6.918

10.  Oocyte Scoring Enhances Embryo-Scoring in Predicting Pregnancy Chances with IVF Where It Counts Most.

Authors:  Emanuela Lazzaroni-Tealdi; David H Barad; David F Albertini; Yao Yu; Vitaly A Kushnir; Helena Russell; Yan-Guang Wu; Norbert Gleicher
Journal:  PLoS One       Date:  2015-12-02       Impact factor: 3.240

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  11 in total

1.  An artificial neural network for the prediction of assisted reproduction outcome.

Authors:  Paraskevi Vogiatzi; Abraham Pouliakis; Charalampos Siristatidis
Journal:  J Assist Reprod Genet       Date:  2019-06-19       Impact factor: 3.412

2.  Discarding IVF embryos: reporting on global practices.

Authors:  M Simopoulou; K Sfakianoudis; P Giannelou; A Rapani; E Maziotis; P Tsioulou; S Grigoriadis; E Simopoulos; D Mantas; M Lambropoulou; M Koutsilieris; K Pantos; J C Harper
Journal:  J Assist Reprod Genet       Date:  2019-12-01       Impact factor: 3.412

3.  Trending in human ARTs: Jumping on the Artificial Intelligence and Machine Learning bandwagon.

Authors:  David F Albertini
Journal:  J Assist Reprod Genet       Date:  2021-07       Impact factor: 3.357

4.  Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies.

Authors:  Gerard Letterie
Journal:  J Assist Reprod Genet       Date:  2021-04-19       Impact factor: 3.357

5.  Using Deep Learning in a Monocentric Study to Characterize Maternal Immune Environment for Predicting Pregnancy Outcomes in the Recurrent Reproductive Failure Patients.

Authors:  Chunyu Huang; Zheng Xiang; Yongnu Zhang; Dao Shen Tan; Chun Kit Yip; Zhiqiang Liu; Yuye Li; Shuyi Yu; Lianghui Diao; Lap Yan Wong; Wai Lim Ling; Yong Zeng; Wenwei Tu
Journal:  Front Immunol       Date:  2021-04-01       Impact factor: 7.561

6.  An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data.

Authors:  Bo Huang; Wei Tan; Zhou Li; Lei Jin
Journal:  Reprod Biol Endocrinol       Date:  2021-12-13       Impact factor: 5.211

7.  Good practice recommendations for the use of time-lapse technology.

Authors:  Susanna Apter; Thomas Ebner; Thomas Freour; Yves Guns; Borut Kovacic; Nathalie Le Clef; Monica Marques; Marcos Meseguer; Debbie Montjean; Ioannis Sfontouris; Roger Sturmey; Giovanni Coticchio
Journal:  Hum Reprod Open       Date:  2020-03-19

Review 8.  Mining of variables from embryo morphokinetics, blastocyst's morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service.

Authors:  Dóris Spinosa Chéles; Eloiza Adriane Dal Molin; José Celso Rocha; Marcelo Fábio Gouveia Nogueira
Journal:  JBRA Assist Reprod       Date:  2020-10-06

Review 9.  Current Advancements in Noninvasive Profiling of the Embryo Culture Media Secretome.

Authors:  Raminta Zmuidinaite; Fady I Sharara; Ray K Iles
Journal:  Int J Mol Sci       Date:  2021-03-03       Impact factor: 5.923

10.  Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations?

Authors:  Nejc Kozar; Vilma Kovač; Milan Reljič
Journal:  J Assist Reprod Genet       Date:  2021-05-24       Impact factor: 3.412

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