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一种用于辅助生殖技术中植入前DNA甲基化筛查胚胎选择的人工智能模型。

An artificial intelligence model for embryo selection in preimplantation DNA methylation screening in assisted reproductive technology.

作者信息

Zhan Jianhong, Chen Chuangqi, Zhang Na, Zhong Shuhuai, Wang Jiaming, Hu Jinzhou, Liu Jiang

机构信息

Institute of Biophysics, Chinese Academy of Science, Beijing 100101, China.

Guangdong Women's and Children's Hospital, Guangzhou 511400, China.

出版信息

Biophys Rep. 2023 Dec 31;9(6):352-361. doi: 10.52601/bpr.2023.230035.

Abstract

Embryo quality is a critical determinant of clinical outcomes in assisted reproductive technology (ART). A recent clinical trial investigating preimplantation DNA methylation screening (PIMS) revealed that whole genome DNA methylation level is a novel biomarker for assessing ART embryo quality. Here, we reinforced and estimated the clinical efficacy of PIMS. We introduce PIMS-AI, an innovative artificial intelligence (AI) based model, to predict the probability of an embryo producing live birth and subsequently assist ART embryo selection. Our model demonstrated robust performance, achieving an area under the curve (AUC) of 0.90 in cross-validation and 0.80 in independent testing. In simulated embryo selection, PIMS-AI attained an accuracy of 81% in identifying viable embryos for patients. Notably, PIMS-AI offers significant advantages over conventional preimplantation genetic testing for aneuploidy (PGT-A), including enhanced embryo discriminability and the potential to benefit a broader patient population. In conclusion, our approach holds substantial promise for clinical application and has the potential to significantly improve the ART success rate.

摘要

胚胎质量是辅助生殖技术(ART)临床结局的关键决定因素。最近一项关于植入前DNA甲基化筛查(PIMS)的临床试验表明,全基因组DNA甲基化水平是评估ART胚胎质量的一种新型生物标志物。在此,我们强化并评估了PIMS的临床疗效。我们引入了PIMS-AI,这是一种基于人工智能(AI)的创新模型,用于预测胚胎活产的概率,进而辅助ART胚胎选择。我们的模型表现出色,在交叉验证中曲线下面积(AUC)达到0.90,在独立测试中达到0.80。在模拟胚胎选择中,PIMS-AI识别患者可行胚胎的准确率达到81%。值得注意的是,与传统的植入前非整倍体基因检测(PGT-A)相比,PIMS-AI具有显著优势,包括增强胚胎辨别能力以及使更广泛患者群体受益的潜力。总之,我们的方法在临床应用方面具有巨大潜力,并有可能显著提高ART成功率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a105/10960573/0214a1f60249/br-9-6-352-1.jpg

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