辅助生殖技术植入前 DNA 甲基化筛查中胚胎选择的人工智能模型。

Jianhong Zhan, Chuangqi Chen, Na Zhang, Shuhuai Zhong, Jiaming Wang, Jinzhou Hu, Jiang Liu
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引用次数: 0

摘要

胚胎质量是决定辅助生殖技术(ART)临床结果的关键因素。最近一项研究植入前 DNA 甲基化筛查(PIMS)的临床试验显示,全基因组 DNA 甲基化水平是评估 ART 胚胎质量的新型生物标志物。在此,我们加强并评估了 PIMS 的临床疗效。我们引入了基于人工智能(AI)的创新模型 PIMS-AI,用于预测胚胎产生活产的概率,从而协助 ART 胚胎的选择。我们的模型表现稳健,交叉验证的曲线下面积(AUC)为 0.90,独立测试的曲线下面积(AUC)为 0.80。在模拟胚胎选择中,PIMS-AI 为患者识别可行胚胎的准确率达到 81%。值得注意的是,与传统的非整倍体植入前基因检测(PGT-A)相比,PIMS-AI 具有显著的优势,包括增强了胚胎鉴别能力,并有可能使更多患者受益。总之,我们的方法具有很大的临床应用前景,并有可能显著提高 ART 的成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An artificial intelligence model for embryo selection in preimplantation DNA methylation screening in assisted reproductive technology.

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.

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