Artificial intelligence in assisted reproductive technology: separating the dream from reality

Reproductive BioMedicine Online, Journal Year: 2025, Volume and Issue: 50(4), P. 104855 - 104855

Published: April 1, 2025

This paper critically reviews the role of artificial intelligence (AI) in assisted reproductive technology (ART), a nascent field that has emerged over last decade. While AI holds immense promise for enhancing IVF efficiency, standardization, and outcomes, its current trajectory reveals significant challenges. Much recent literature presents variations on established methodologies rather than groundbreaking advancements, with many studies lacking clear clinical applications or outcome-driven validations. Moreover, growing enthusiasm ART is often accompanied by undue hype obscures realistic potential fosters inflated expectations. Despite these limitations, AI-driven innovations such as advanced image analysis, personalized protocols, automation embryology workflows are beginning to show value. Machine learning algorithms robotics may help address inefficiencies, alleviate staff shortages, improve decision-making laboratory. However, progress tempered drawbacks including ethical concerns, limited transparency systems, regulatory impediments. Data-sharing barriers our hinder tool development significantly. Energy-intensive computational processes expanding data centers also raise sustainability underscoring need environmentally responsible development. As evolves, it must emphasize rigorous validation, collaborative frameworks, alignment needs practitioners patients.

Language: Английский

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Opportunities and limitations of introducing artificial intelligence technologies into reproductive medicine

Obstetrics Gynecology and Reproduction, Journal Year: 2025, Volume and Issue: unknown

Published: March 14, 2025

Given the increasing problem of infertility in Russian Federation, assisted reproductive technologies (ART) have proven to be one most effective treatments for this condition. Notably, introduction ART methods, particularly vitro fertilization (IVF), has led markedly increased birth rates over past two decades. Studies show that machine learning algorithms can process images embryos assess their quality, thus facilitating selection viable among them transfer. There are ethical and technical barriers hindering widespread adoption artificial intelligence (AI) clinical practice, including concerns data privacy as well a need train specialists deal with new technologies. AI analyze vast amounts data, medical histories research results, more accurately predict pregnancy outcomes. This enables doctors make justified decisions. In future, will able patient efficiently, helping identify causes at earlier stages.

Language: Английский

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Deep learning applications for human embryo assessment using time-lapse imaging: scoping review

Frontiers in Reproductive Health, Journal Year: 2025, Volume and Issue: 7

Published: April 8, 2025

The integration of deep learning (DL) and time-lapse imaging technologies offers new possibilities for improving embryo assessment selection in clinical vitro Fertilization (IVF). This scoping review aims to explore the range model applications evaluation embryos monitored through systems. A total 6 electronic databases (Scopus, MEDLINE, EMBASE, ACM Digital Library, IEEE Xplore, Google Scholar) were searched peer-reviewed literature published before May 2024. We adhered PRISMA guidelines reporting reviews. Out 773 articles reviewed, 77 met inclusion criteria. Over past four years, use DL analysis has increased rapidly. primary reviewed studies included predicting development quality (61%, n = 47) forecasting outcomes, such as pregnancy implantation (35%, 27). number involved exhibited significant variation, with a mean 10,485 (SD 35,593) from 20 249,635 embryos. variety data types have been used, namely images blastocyst-stage (47%, 36), followed by combined cleavage blastocyst stages (23%, 18). Most did not provide maternal age details (82%, 63). Convolutional neural networks (CNNs) predominant architecture accounting 81% (n 62) studies. All utilized video (100%) training data, while some also incorporated demographics, reproductive histories, IVF cycle parameters. accuracy discriminative measure (58%, 45). Our results highlight diverse potential suggest directions future advancements techniques.

Language: Английский

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A review of artificial intelligence applications in in vitro fertilization

Journal of Assisted Reproduction and Genetics, Journal Year: 2024, Volume and Issue: 42(1), P. 3 - 14

Published: Oct. 14, 2024

Language: Английский

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Artificial intelligence in human reproduction

Archives of Medical Research, Journal Year: 2024, Volume and Issue: 55(8), P. 103131 - 103131

Published: Nov. 29, 2024

Language: Английский

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Unpacking the artificial intelligence toolbox for embryo ploidy prediction

Human Reproduction, Journal Year: 2023, Volume and Issue: 38(12), P. 2538 - 2542

Published: Oct. 24, 2023

Graphical AbstractOpen in new tabDownload slideThe May ESHRE Journal Club discussion focused on a study byBamford et al. (2023), ML models for predicting ploidy status of embryos, IVF dataset characteristics and imbalances, the use ML-based embryo assessment as tool clinical decisions. ML, machine learning.

Language: Английский

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External validation of a fully automated evaluation tool: a retrospective analysis on 68,471 scored embryos.

Fertility and Sterility, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

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