A brief history of artificial intelligence embryo selection: From black-box to glass-box
Document Type
Journal Article
Publication Title
Human Reproduction
Volume
39
Issue
2
First Page
285
Last Page
292
PubMed ID
38061074
Publisher
Oxford University Press
School
School of Medical and Health Sciences
RAS ID
64520
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.
DOI
10.1093/humrep/dead254
Access Rights
free_to_read
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