Automation, Artificial Intelligence and Innovations in the Future of IVF

Varghese, Alex C.; Siristatidis, Charalampos

doi:10.1007/978-3-319-43011-9_71

Cited by 2 publications

(4 citation statements)

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“…One of the current topics in reproductive medicine is testing the introduction of artificial intelligence (AI) (i.e. the automation of decision-making processes) into some procedures related to assisted reproduction (Varghese and Charalampos 2019;Zaninovic and Rosenwaks 2020;Casciani et al 2021). It is also a fact that many research institutions are in the process of developing and refining procedures that would ensure the earliest and best possible extracorporeal development of foetuses (Partridge et al 2017;Bulletti and Simon 2019;Segers 2021).…”

Section: The Third Wave Of the Reproductive Revolutionmentioning

confidence: 99%

“…Proponents of this idea often point to the need for a judicious consideration of the implications of changing standards in assisted reproduction (Kragh and Karstoft 2021). Outcomes are also a fundamental topic of debate about the future of assisted reproduction, as a growing number of actors in this debate assert that the proper automation of assisted reproduction could dramatically increase its success rate while significantly reducing the costs of application (Meseguer et al 2012;Varghese and Charalampos 2019;Casciani et al 2021). The implementation of robotics and AI is considered to be the most promising route to such an outcome.…”

Section: The Automation Of Reproductionmentioning

confidence: 99%

“…Such a hypothetical form of human reproduction will be herein referred to as robogenesis 2 (for an explanation of the term, see Section 1). We are still far away from achieving robogenesis; nevertheless, even today some research teams are working on experimental systems that could automate and robotize procedures related to assisted reproduction and gestation (Meseguer et al 2012;Partridge et al 2017;Varghese and Charalampos 2019;Casciani et al 2021). Certainly, it is difficult to predict whether these experimental approaches will ever make it into clinical practice; however, it is not impossible to imagine a future where a synergistic combination of advanced reproductive technologies could enable fully automated external human reproduction (robogenesis).…”

Section: Introductionmentioning

confidence: 99%

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The Blurring of Boundaries in Bioscientific Discourse

2021

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Today artificial intelligence is being used experimentally to predict embryo viability. Some research teams have been also looking into the idea of the automation and robotization of other procedures that are frequently used in assisted reproduction. For a long time, other groups of researchers have been working on biotechnological devices that could ensure ectogenic gestation (ectogenesis). It is therefore imaginable that a synergistic combination of these and other reproductive technologies could one day lead to a complex procedure that would enable fully automated external human reproduction (herein referred to as robogenesis). This chapter aims to introduce the basic conceptual and ontological aspects of robogenesis. The first part is dedicated to defining robogenesis and situating it within the third wave of the reproductive revolution. The second part focuses on the analysis of theoretical, technological, and narrative assumptions about robogenesis. The final part presents arguments in favour of practising robogenesis. I will argue that a complete automation of human reproduction may be acceptable from the perspectives of humanism, transhumanism, and posthumanism, albeit for diametrically different ontological reasons.

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Section: The Third Wave Of the Reproductive Revolutionmentioning

confidence: 99%

Section: The Automation Of Reproductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Blurring of Boundaries in Bioscientific Discourse

2021

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“…ANNs also play a vital role in IVF; however, they have several limitations and flaws in terms of objectivity and training. In their current form, ANNs cannot replace embryologists; however, they can assist embryologists in supporting clinical decisions [15]. Two firms, from Sweden and Australia, working on IVF, have employed AI-based software to assist in embryo selection procedures; however, these are experimental setups and are in the pipeline for clinical validation [16].…”

Section: Introductionmentioning

confidence: 99%

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization

Ishaq¹,

Raza²,

Rehar³

et al. 2023

Mathematics

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The increasing global infertility rate is a matter of significant concern. In vitro fertilization (IVF) significantly minimizes infertility by providing an alternative clinical means of becoming pregnant. The success of IVF mainly depends on the assessment and analysis of human blastocyst components such as the blastocoel (BC), zona pellucida (ZP), inner cell mass (ICM), and trophectoderm (TE). Embryologists perform a morphological assessment of the blastocyst components for the selection of potential embryos to be used in the IVF process. Manual assessment of blastocyst components is time-consuming, subjective, and prone to errors. Therefore, artificial intelligence (AI)-based methods are highly desirable for enhancing the success rate and efficiency of IVF. In this study, a novel feature-supplementation-based blastocyst segmentation network (FSBS-Net) has been developed to deliver higher segmentation accuracy for blastocyst components with less computational overhead compared with state-of-the-art methods. FSBS-Net uses an effective feature supplementation mechanism along with ascending channel convolutional blocks to accurately detect the pixels of the blastocyst components with minimal spatial loss. The proposed method was evaluated using an open database for human blastocyst component segmentation, and it outperformed state-of-the-art methods in terms of both segmentation accuracy and computational efficiency. FSBS-Net segmented the BC, ZP, ICM, TE, and background with intersections over union (IoU) values of 89.15, 85.80, 85.55, 80.17, and 95.61%, respectively. In addition, FSBS-Net achieved a mean IoU for all categories of 87.26% with only 2.01 million trainable parameters. The experimental results demonstrate that the proposed method could be very helpful in assisting embryologists in the morphological assessment of human blastocyst components.

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Automation, Artificial Intelligence and Innovations in the Future of IVF

Cited by 2 publications

References 50 publications

The Blurring of Boundaries in Bioscientific Discourse

The Blurring of Boundaries in Bioscientific Discourse

Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization

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