基于人工智能和延时图像序列的稳健和可推广的胚胎选择
Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences
摘要：
评估和选择最可行的胚胎进行移植是体外受精（IVF）的重要组成部分。近年来，已经提出了几种方法来使用人工智能（AI）和深度学习来改进和自动化该过程。基于具有已知植入数据（KID）的胚胎图像，人工智能模型已经被训练成自动对胚胎进行评分，这些评分与胚胎成功植入的机会有关。然而，到目前为止，只进行了有限的研究来评估胚胎选择模型如何推广到新的诊所，以及它们如何在各种条件下进行亚组分析。在本文中，我们研究了仅使用延时图像序列的基于深度学习的胚胎选择模型在不同患者年龄和临床条件下的表现，以及它如何与传统的形态动力学参数相关。该模型基于来自18个IVF中心的115，832个胚胎的大型数据集进行训练和评估，其中14，644个胚胎是移植的KID胚胎。在一个独立的测试集中，AI模型对接受者操作特征曲线的曲线下面积（AUC）为0.67的KID胚胎和AUC为0.95的所有胚胎进行了分类。临床坚持测试表明，该模型推广到新的诊所，KID胚胎的AUC范围为0.60-0.75。在年龄、授精方法、孵育时间和转移方案的不同亚组中，AUC范围为0.63 - 0.69。此外，模型预测与囊胚分级呈正相关，与直接卵裂呈负相关。全自动iDAScore v1.0模型的表现至少与最先进的手动胚胎选择模型一样好。此外，胚胎评分的完全自动化意味着更少的手动评估，并消除了由于观察者间和观察者内变化而产生的偏差。
英文摘要：
Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60-0.75 for KID embryos. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
