Turning the Black Box Into a Glass Box: Use of Transparent Artificial Intelligence to Understand Biological Markers Useful for Embryo Selection

The prospect of artificial intelligence to personalize assisted reproductive technology

“…Recently, a biomarker-scoring CDSS based on 799 blastocyst videos, CHLOE EQ ™ (Fairtility), has been described and takes into account patient and embryo data including blastocyst diameter, degree, and time of expansion, and other morphokinetic markers. Though preliminary results are promising, these new systems still require external validation and larger-scale prospective studies before widespread adoption to realize the end goal of fully automated blastocyst assessment and accurate embryo prognosis 94,95 . It is paramount that future algorithms focus not only on the competitive selection of the best embryos for culture and transfer but also can differentiate between embryos that are otherwise morphologically indistinguishable to the naked eye, wherein the real challenge lies.…”

“…A predominant barrier to adoption is trustworthiness, especially with 'black-box' AI systems 29 , which has led to transparency being a key characteristic preferred by clinicians as such models offer simpler interpretations, although may compromise accuracy when applied to more complicated learning tasks 28 . Implementations of 'black-box' models are evolving, especially for embryological analyses, due to the data being primarily image-based; in turn, efforts in explainability have emerged to seek insights for model generalizability, fairness, and trustworthiness 94,95 . Misleading conclusions may be reached if clinical inference is neglected during the decision-making process since such methods are often correlation-based and prone to 'overfitting' 120 .…”