Literature DB >> 32654105

Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data.

Eleonora Inácio Fernandez1, André Satoshi Ferreira1, Matheus Henrique Miquelão Cecílio1, Dóris Spinosa Chéles1,2, Rebeca Colauto Milanezi de Souza1, Marcelo Fábio Gouveia Nogueira2, José Celso Rocha3,4.   

Abstract

Over the past years, the assisted reproductive technologies (ARTs) have been accompanied by constant innovations. For instance, intracytoplasmic sperm injection (ICSI), time-lapse monitoring of the embryonic morphokinetics, and PGS are innovative techniques that increased the success of the ART. In the same trend, the use of artificial intelligence (AI) techniques is being intensively researched whether in the embryo or spermatozoa selection. Despite several studies already published, the use of AI within assisted reproduction clinics is not yet a reality. This is largely due to the different AI techniques that are being proposed to be used in the daily routine of the clinics, which causes some uncertainty in their use. To shed light on this complex scenario, this review briefly describes some of the most frequently used AI algorithms, their functionalities, and their potential use. Several databases were analyzed in search of articles where applied artificial intelligence algorithms were used on reproductive data. Our focus was on the classification of embryonic cells and semen samples. Of a total of 124 articles analyzed, 32 were selected for this review. From the proposed algorithms, most have achieved a satisfactory precision, demonstrating the potential of a wide range of AI techniques. However, the evaluation of these studies suggests the need for more standardized research to validate the proposed models and their algorithms. Routine use of AI in assisted reproduction clinics is just a matter of time. However, the choice of AI technique to be used is supported by a better understanding of the principles subjacent to each technique, that is, its robustness, pros, and cons. We provide some current (although incipient) and potential uses of AI on the clinic routine, discussing how accurate and friendly it could be. Finally, we propose some standards for AI research on the selection of the embryo to be transferred and other future hints. For us, the imminence of its use is evident, providing a revolutionary milestone that will impact the ART.

Entities:  

Keywords:  Artificial intelligence; Assisted reproductive technologies; Deep learning; Embryo classification; Multilayer perceptron; Prediction models

Mesh:

Year:  2020        PMID: 32654105      PMCID: PMC7550511          DOI: 10.1007/s10815-020-01881-9

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


  29 in total

1.  A logical calculus of the ideas immanent in nervous activity. 1943.

Authors:  W S McCulloch; W Pitts
Journal:  Bull Math Biol       Date:  1990       Impact factor: 1.758

2.  How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis.

Authors:  Robert Milewski; Agnieszka Kuczyńska; Bożena Stankiewicz; Waldemar Kuczyński
Journal:  Adv Med Sci       Date:  2017-04-03       Impact factor: 3.287

3.  Artificial intelligence techniques for embryo and oocyte classification.

Authors:  Claudio Manna; Loris Nanni; Alessandra Lumini; Sebastiana Pappalardo
Journal:  Reprod Biomed Online       Date:  2012-10-02       Impact factor: 3.828

4.  Pregnancy prediction models and eSET criteria for IVF patients--do we need more information?

Authors:  Lars D M Ottosen; Ulrik Kesmodel; Johnny Hindkjaer; Hans Jakob Ingerslev
Journal:  J Assist Reprod Genet       Date:  2006-12-13       Impact factor: 3.412

5.  Semen parameters can be predicted from environmental factors and lifestyle using artificial intelligence methods.

Authors:  Jose L Girela; David Gil; Magnus Johnsson; María José Gomez-Torres; Joaquín De Juan
Journal:  Biol Reprod       Date:  2013-04-18       Impact factor: 4.285

6.  Bayesian classification for the selection of in vitro human embryos using morphological and clinical data.

Authors:  Dinora Araceli Morales; Endika Bengoetxea; Pedro Larrañaga; Miguel García; Yosu Franco; Mónica Fresnada; Marisa Merino
Journal:  Comput Methods Programs Biomed       Date:  2008-01-10       Impact factor: 5.428

7.  Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image.

Authors:  Yasunari Miyagi; Toshihiro Habara; Rei Hirata; Nobuyoshi Hayashi
Journal:  Reprod Med Biol       Date:  2019-02-19

8.  Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer.

Authors:  D Tran; S Cooke; P J Illingworth; D K Gardner
Journal:  Hum Reprod       Date:  2019-06-04       Impact factor: 6.918

9.  Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization.

Authors:  Zev Rosenwaks; Olivier Elemento; Nikica Zaninovic; Iman Hajirasouliha; Pegah Khosravi; Ehsan Kazemi; Qiansheng Zhan; Jonas E Malmsten; Marco Toschi; Pantelis Zisimopoulos; Alexandros Sigaras; Stuart Lavery; Lee A D Cooper; Cristina Hickman; Marcos Meseguer
Journal:  NPJ Digit Med       Date:  2019-04-04

10.  Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF.

Authors:  M VerMilyea; J M M Hall; S M Diakiw; A Johnston; T Nguyen; D Perugini; A Miller; A Picou; A P Murphy; M Perugini
Journal:  Hum Reprod       Date:  2020-04-28       Impact factor: 6.918

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  12 in total

Review 1.  Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF.

Authors:  Claudio Michael Louis; Alva Erwin; Nining Handayani; Arie A Polim; Arief Boediono; Ivan Sini
Journal:  J Assist Reprod Genet       Date:  2021-04-03       Impact factor: 3.357

2.  Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination.

Authors:  Viktorija Valiuškaitė; Vidas Raudonis; Rytis Maskeliūnas; Robertas Damaševičius; Tomas Krilavičius
Journal:  Sensors (Basel)       Date:  2020-12-24       Impact factor: 3.576

3.  Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.

Authors:  Bo Huang; Shunyuan Zheng; Bingxin Ma; Yongle Yang; Shengping Zhang; Lei Jin
Journal:  BMC Pregnancy Childbirth       Date:  2022-01-16       Impact factor: 3.007

4.  Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm.

Authors:  Ran Liu; Shun Bai; Xiaohua Jiang; Lihua Luo; Xianhong Tong; Shengxia Zheng; Ying Wang; Bo Xu
Journal:  Front Endocrinol (Lausanne)       Date:  2021-11-02       Impact factor: 5.555

5.  Interpretable, not black-box, artificial intelligence should be used for embryo selection.

Authors:  Michael Anis Mihdi Afnan; Yanhe Liu; Vincent Conitzer; Cynthia Rudin; Abhishek Mishra; Julian Savulescu; Masoud Afnan
Journal:  Hum Reprod Open       Date:  2021-11-02

6.  Machine Learning-Based Modeling of Ovarian Response and the Quantitative Evaluation of Comprehensive Impact Features.

Authors:  Liu Liu; Fujin Shen; Hua Liang; Zhe Yang; Jing Yang; Jiao Chen
Journal:  Diagnostics (Basel)       Date:  2022-02-14

7.  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

8.  Does artificial intelligence have a role in the IVF clinic?

Authors:  Darren J X Chow; Philip Wijesinghe; Kishan Dholakia; Kylie R Dunning
Journal:  Reprod Fertil       Date:  2021-08-23

Review 9.  Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis.

Authors:  Konstantinos Sfakianoudis; Evangelos Maziotis; Sokratis Grigoriadis; Agni Pantou; Georgia Kokkini; Anna Trypidi; Polina Giannelou; Athanasios Zikopoulos; Irene Angeli; Terpsithea Vaxevanoglou; Konstantinos Pantos; Mara Simopoulou
Journal:  Biomedicines       Date:  2022-03-17

10.  Embryo selection with artificial intelligence: how to evaluate and compare methods?

Authors:  Mikkel Fly Kragh; Henrik Karstoft
Journal:  J Assist Reprod Genet       Date:  2021-06-26       Impact factor: 3.412

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