A brief history of artificial intelligence embryo selection: From black-box to glass-box

Abstract

With the exponential growth of computing power and accumulation of embryo image data in recent years, artificial intelligence (AI) is starting to be utilized in embryo selection in IVF. Amongst different AI technologies, machine learning (ML) has the potential to reduce operator-related subjectivity in embryo selection while saving labor time on this task. However, as modern deep learning (DL) techniques, a subcategory of ML, are increasingly used, its integrated black-box attracts growing concern owing to the well-recognized issues regarding lack of interpretability. Currently, there is a lack of randomized controlled trials to confirm the effectiveness of such black-box models. Recently, emerging evidence has shown underperformance of black-box models compared to the more interpretable traditional ML models in embryo selection. Meanwhile, glass-box AI, such as interpretable ML, is being increasingly promoted across a wide range of fields and is supported by its ethical advantages and technical feasibility. In this review, we propose a novel classification system for traditional and AI-driven systems from an embryology standpoint, defining different morphology-based selection approaches with an emphasis on subjectivity, explainability, and interpretability.

RAS ID

64520

Document Type

Journal Article

Date of Publication

2-1-2024

Volume

39

Issue

2

PubMed ID

38061074

School

School of Medical and Health Sciences

Copyright

free_to_read

Publisher

Oxford University Press

Comments

Lee, T., Natalwala, J., Chapple, V., & Liu, Y. (2024). A brief history of artificial intelligence embryo selection: From black-box to glass-box. Human Reproduction, 39(2), 285-292. https://doi.org/10.1093/humrep/dead254

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Link to publisher version (DOI)

10.1093/humrep/dead254