融合视频与病历数据，自动预测胚胎移植成功率。
Multimodal Learning for Embryo Viability Prediction in Clinical IVF
- 结合时间流逝视频与电子病历，多模态建模提升预测能力。
- 相比单一模态，多模态融合显著提高胚胎存活率预测准确率。
- 适合辅助生殖临床决策，减轻医生负担。
在体外受精（IVF）临床实践中，准确识别最具活力的胚胎进行移植，是提高妊娠成功率的关键。传统方法依赖胚胎学家通过光学显微镜在特定时间点手动评估胚胎的静态形态特征，该过程耗时、成本高且主观性强，导致选择结果存在较大差异。为解决上述问题，我们提出一种融合时间流逝视频数据与电子健康记录（EHRs）的多模态模型，用于预测胚胎活力。研究中面临的核心挑战是如何有效融合具有本质差异的视频与病历数据。我们系统评估了多种模态输入组合与融合策略。所提方法可实现大规模临床IVF场景下的快速、自动化胚胎活力预测。
原文摘要 · Abstract (English)
In clinical In-Vitro Fertilization (IVF), identifying the most viable embryo for transfer is important to increasing the likelihood of a successful pregnancy. Traditionally, this process involves embryologists manually assessing embryos' static morphological features at specific intervals using light microscopy. This manual evaluation is not only time-intensive and costly, due to the need for expert analysis, but also inherently subjective, leading to variability in the selection process. To address these challenges, we develop a multimodal model that leverages both time-lapse video data and Electronic Health Records (EHRs) to predict embryo viability. One of the primary challenges of our research is to effectively combine time-lapse video and EHR data, owing to their inherent differences in modality. We comprehensively analyze our multimodal model with various modality inputs and integration approaches. Our approach will enable fast and automated embryo viability predictions in scale for clinical IVF.
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