Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment

TL;DR

This paper presents an explainable AI framework combining CNN and LSTM to classify embryo images in IVF, improving accuracy and interpretability over traditional methods.

Contribution

It introduces a novel CNN-LSTM model with explainability for embryo classification, enhancing efficiency and transparency in IVF embryo selection.

Findings

High accuracy in embryo classification

Enhanced interpretability with XAI techniques

Potential to improve IVF success rates

Abstract

Infertility has a considerable impact on individuals' quality of life, affecting them socially and psychologically, with projections indicating a rise in the upcoming years. In vitro fertilization (IVF) emerges as one of the primary techniques within economically developed nations, employed to address the rising problem of low fertility. Expert embryologists conventionally grade embryos by reviewing blastocyst images to select the most optimal for transfer, yet this process is time-consuming and lacks efficiency. Blastocyst images provide a valuable resource for assessing embryo viability. In this study, we introduce an explainable artificial intelligence (XAI) framework for classifying embryos, employing a fusion of convolutional neural network (CNN) and long short-term memory (LSTM) architecture, referred to as CNN-LSTM. Utilizing deep learning, our model achieves high accuracy in embryo classification while maintaining interpretability through XAI.