Literature DB >> 35922741

Automation in ART: Paving the Way for the Future of Infertility Treatment.

Kadrina Abdul Latif Abdullah1, Tomiris Atazhanova1, Alejandro Chavez-Badiola2, Sourima Biswas Shivhare3,4.   

Abstract

In vitro fertilisation (IVF) is estimated to account for the birth of more than nine million babies worldwide, perhaps making it one of the most intriguing as well as commoditised and industrialised modern medical interventions. Nevertheless, most IVF procedures are currently limited by accessibility, affordability and most importantly multistep, labour-intensive, technically challenging processes undertaken by skilled professionals. Therefore, in order to sustain the exponential demand for IVF on one hand, and streamline existing processes on the other, innovation is essential. This may not only effectively manage clinical time but also reduce cost, thereby increasing accessibility, affordability and efficiency. Recent years have seen a diverse range of technologies, some integrated with artificial intelligence, throughout the IVF pathway, which promise personalisation and, at least, partial automation in the not-so-distant future. This review aims to summarise the rapidly evolving state of these innovations in automation, with or without the integration of artificial intelligence, encompassing the patient treatment pathway, gamete/embryo selection, endometrial evaluation and cryopreservation of gametes/embryos. Additionally, it shall highlight the resulting prospective change in the role of IVF professionals and challenges of implementation of some of these technologies, thereby aiming to motivate continued research in this field.
© 2022. The Author(s).

Entities:  

Year:  2022        PMID: 35922741     DOI: 10.1007/s43032-022-00941-y

Source DB:  PubMed          Journal:  Reprod Sci        ISSN: 1933-7191            Impact factor:   2.924


  71 in total

1.  Antimüllerian hormone levels and antral follicle count as prognostic indicators in a personalized prediction model of live birth.

Authors:  Scott M Nelson; Richard Fleming; Marco Gaudoin; Bokyung Choi; Kenny Santo-Domingo; Mylene Yao
Journal:  Fertil Steril       Date:  2015-05-21       Impact factor: 7.329

Review 2.  Automation, machine learning, and artificial intelligence in echocardiography: A brave new world.

Authors:  Sumeet Gandhi; Wassim Mosleh; Joshua Shen; Chi-Ming Chow
Journal:  Echocardiography       Date:  2018-07-05       Impact factor: 1.724

3.  Comparison of antimüllerian hormone levels and antral follicle count as predictor of ovarian response to controlled ovarian stimulation in good-prognosis patients at individual fertility clinics in two multicenter trials.

Authors:  Scott M Nelson; Bjarke M Klein; Joan-Carles Arce
Journal:  Fertil Steril       Date:  2015-01-24       Impact factor: 7.329

Review 4.  Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Authors:  Carol Lynn Curchoe; Jonas Malmsten; Charles Bormann; Hadi Shafiee; Adolfo Flores-Saiffe Farias; Gerardo Mendizabal; Alejandro Chavez-Badiola; Alexandros Sigaras; Hoor Alshubbar; Jerome Chambost; Celine Jacques; Chris-Alexandre Pena; Andrew Drakeley; Thomas Freour; Iman Hajirasouliha; Cristina Fontes Lindemann Hickman; Olivier Elemento; Nikica Zaninovic; Zev Rosenwaks
Journal:  Fertil Steril       Date:  2020-11       Impact factor: 7.329

5.  AI in the treatment of fertility: key considerations.

Authors:  Jason Swain; Matthew Tex VerMilyea; Marcos Meseguer; Diego Ezcurra
Journal:  J Assist Reprod Genet       Date:  2020-09-29       Impact factor: 3.412

Review 6.  Fertility and infertility: Definition and epidemiology.

Authors:  Mélodie Vander Borght; Christine Wyns
Journal:  Clin Biochem       Date:  2018-03-16       Impact factor: 3.281

Review 7.  Trends of male factor infertility, an important cause of infertility: A review of literature.

Authors:  Naina Kumar; Amit Kant Singh
Journal:  J Hum Reprod Sci       Date:  2015 Oct-Dec

Review 8.  Artificial intelligence in reproductive medicine.

Authors:  Renjie Wang; Wei Pan; Lei Jin; Yuehan Li; Yudi Geng; Chun Gao; Gang Chen; Hui Wang; Ding Ma; Shujie Liao
Journal:  Reproduction       Date:  2019-10       Impact factor: 3.906

9.  PIVET rFSH dosing algorithms for individualized controlled ovarian stimulation enables optimized pregnancy productivity rates and avoidance of ovarian hyperstimulation syndrome.

Authors:  John L Yovich; Birgit Alsbjerg; Jason L Conceicao; Peter M Hinchliffe; Kevin N Keane
Journal:  Drug Des Devel Ther       Date:  2016-08-10       Impact factor: 4.162

10.  A Preliminary Experience of Integration of an Electronic Witness System, its Validation, Efficacy on Lab Performance, and Staff Satisfaction Assessment in a Busy Indian in vitro Fertilization Laboratory.

Authors:  Sweta Gupta; Ashish Fauzdar; Vikram Jeet Singh; Ajay Srivastava; Kamlesh Sharma; Sabina Singh
Journal:  J Hum Reprod Sci       Date:  2020-12-28
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