Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, NY, USA.
Caryl and Israel Englander Institute for Precision Medicine, The Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
Nat Commun. 2024 Sep 5;15(1):7756. doi: 10.1038/s41467-024-51823-7.
Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver operating characteristic curve of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists' manual scores. While not a replacement for preimplantation genetic testing for aneuploidy, BELA exemplifies how such models can streamline the embryo evaluation process.
评估受精卵对于体外受精至关重要，而人工智能正在彻底改变这一任务。通过有效地利用延时成像来识别关键的发育时间点，以最大程度地提高预测准确性，现有的胚胎质量评估和倍性检测模型可以得到显著改进。为了解决这个问题，我们在不同的胚胎发育阶段开发和比较了各种胚胎倍性状态预测模型。我们提出了 BELA，这是一种最先进的倍性预测模型，它超越了以前基于图像和视频的模型，而不需要胚胎学家的输入。BELA 使用多任务学习来预测质量分数，然后再利用这些分数来预测倍性状态。BELA 在威尔康奈尔数据集上区分整倍体和非整倍体胚胎的接收者操作特征曲线下面积达到 0.76，与基于胚胎学家手动评分训练的模型性能相匹配。虽然 BELA 不能替代胚胎植入前的非整倍体基因检测，但它展示了这类模型如何简化胚胎评估过程。
