用AI模型辅助胚胎筛选，提升试管婴儿成功率。
Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment
- 融合CNN与LSTM的可解释AI模型分析胚胎图像。
- 模型分类准确率高，且能说明判断依据。
- 适合辅助生殖医学医生做决策参考。
不孕不育对个人生活质量有显著影响，带来社会和心理压力，未来患病人数可能上升。体外受精（IVF）是经济发达国家应对生育力下降的主要手段之一。传统上，胚胎学家通过观察囊胚图像进行胚胎分级，以选择最佳胚胎移植，但该过程耗时且效率低。囊胚图像蕴含评估胚胎活力的重要信息。本研究提出一种可解释人工智能（XAI）框架，采用卷积神经网络（CNN）与长短期记忆网络（LSTM）融合架构（称为CNN-LSTM），实现胚胎分类。该模型在保持高分类准确率的同时，借助XAI技术提供决策解释，增强临床可信度。
原文摘要 · Abstract (English)
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.
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