TY - JOUR
T1 - A brief history of artificial intelligence embryo selection
T2 - from black-box to glass-box
AU - Lee, Tammy
AU - Natalwala, Jay
AU - Chapple, Vincent
AU - Liu, Yanhe
PY - 2024/2
Y1 - 2024/2
N2 - 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.Graphical Abstract A proposed classification system for artificial intelligence embryo selection models with different subjectivity, interpretability, and explainability.
AB - 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.Graphical Abstract A proposed classification system for artificial intelligence embryo selection models with different subjectivity, interpretability, and explainability.
KW - Artificial intelligence
KW - Black-box
KW - Deep learning
KW - Embryo selection
KW - Explainability
KW - Glass-box
KW - Interpretability
KW - Machine learning
KW - Subjectivity
KW - Time-lapse videography
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=uwapure5-25&SrcAuth=WosAPI&KeyUT=WOS:001115081500001&DestLinkType=FullRecord&DestApp=WOS
U2 - 10.1093/humrep/dead254
DO - 10.1093/humrep/dead254
M3 - Review article
C2 - 38061074
SN - 0268-1161
VL - 39
SP - 285
EP - 292
JO - Human Reproduction
JF - Human Reproduction
IS - 2
ER -