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

• URL: http://arxiv.org/abs/2506.06680v2
• Date: Mon, 23 Jun 2025 05:29:59 GMT
• Title: Interpretation of Deep Learning Model in Embryo Selection for In Vitro Fertilization (IVF) Treatment
• Authors: Radha Kodali, Venkata Rao Dhulipalla, Venkata Siva Kishor Tatavarty, Madhavi Nadakuditi, Bharadwaj Thiruveedhula, Suryanarayana Gunnam, Durga Prasad Bavirisetti,
• Abstract summary: Expert embryologists conventionally grade embryos by reviewing blastocyst images to select the most optimal for transfer.<n>In this study, we introduce an explainable artificial intelligence framework for classifying embryos, employing a fusion of convolutional neural network (CNN) and long short-term memory (LSTM) architecture.<n>Our model achieves high accuracy in embryo classification while maintaining interpretability through XAI.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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