Explainable Artificial Intelligence for Human Embryo Cell Cleavage Stages Analysis

Abstract

Many couples struggle with infertility, and opt for assisted reproductive technology (ART). Selecting the embryo with the highest chance of resulting in pregnancy, is one of the most critical steps of the ART procedure. The implantation potential of an embryo is associated with its morphology which includes assessing cell cleavage stages. Today, the assessment is mainly done manually by visual examination by embryologist and therefore is often subjective. Deep learning (DL) models can be used to automatically classify cell cleavage stages, and make the embryo assessment process efficient, objective. However, it is important that embryologists understand and trust the DL predictions, and in this paper we evaluate the potential of three explainable artificial intelligence (XAI) techniques. First, we compare the two XAI techniques Grad-CAM and LIME to identify important characteristics in embryo images associated with specific embryo cleavage stages. Both approaches identified biologically relevant morphological characteristics, but generally Grad-CAM was more consistent than LIME. Secondly, we suggest an approach on how to use the XAI technique SHAP to identify image characteristics that caused some images to be misclassified. The identified image characteristics had meaningful biological interpretation such as fragmentation. Overall the study demonstrates that DL in combination XAI can be useful in embryo assessment and selection.

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