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Article Abstract
Background: The integration of deep learning (DL) and time-lapse imaging technologies offers new possibilities for improving embryo assessment and selection in clinical Fertilization (IVF).
Objectives: This scoping review aims to explore the range of deep learning model applications in the evaluation and selection of embryos monitored through time-lapse imaging systems.
Methods: A total of 6 electronic databases (Scopus, MEDLINE, EMBASE, ACM Digital Library, IEEE Xplore, and Google Scholar) were searched for peer-reviewed literature published before May 2024. We adhered to the PRISMA guidelines for reporting scoping reviews.
Results: Out of the 773 articles reviewed, 77 met the inclusion criteria. Over the past four years, the use of DL in embryo analysis has increased rapidly. The primary applications of DL in the reviewed studies included predicting embryo development and quality (61%, = 47) and forecasting clinical outcomes, such as pregnancy and implantation (35%, = 27). The number of embryos involved in the studies exhibited significant variation, with a mean of 10,485 (SD = 35,593) and a range from 20 to 249,635 embryos. A variety of data types have been used, namely images of blastocyst-stage embryos (47%, = 36), followed by combined images of cleavage and blastocyst stages (23%, = 18). Most of the studies did not provide maternal age details (82%, = 63). Convolutional neural networks (CNNs) were the predominant deep learning architecture used, accounting for 81% ( = 62) of the studies. All studies utilized time-lapse video images (100%) as training data, while some also incorporated demographics, clinical and reproductive histories, and IVF cycle parameters. Most studies utilized accuracy as the discriminative measure (58%, = 45).
Conclusion: Our results highlight the diverse applications and potential of deep learning in clinical IVF and suggest directions for future advancements in embryo evaluation and selection techniques.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12011738 | PMC |
| http://dx.doi.org/10.3389/frph.2025.1549642 | DOI Listing |
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