He Hui, Wu Li, Chen Yulin, Li Tuan, Ren Xinling, Hu Juan, Liu Jinming, Chen Wen, Ma Bingxin, Zou Yangyun, Liu Zhen, Lu Sijia, Huang Bo, Jin Lei
Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, China.
Yikon Genomics Company, Ltd., Shanghai, 201499, China.
Heliyon. 2024 Apr 24;10(9):e30189. doi: 10.1016/j.heliyon.2024.e30189. eCollection 2024 May 15.
The selection of the finest possible embryo in in-vitro fertilization (IVF) was crucial and revolutionary, particularly when just one embryo is transplanted to lessen the possibility of multiple pregnancies. However, practical usefulness of currently used methodologies may be constrained. Here, we established a novel non-invasive embryo evaluation method that combines non-invasive chromosomal screening (NICS) and Timelapse system along with artificial intelligence algorithms. With an area under the curve (AUC) of 0.94 and an accuracy of 0.88, the NICS-Timelapse model was able to predict blastocyst euploidy. The performance of the model was further evaluated using 75 patients in various clinical settings. The clinical pregnancy and live birth rates of embryos predicted by the NICS-Timelapse model, showing that embryos with higher euploid probabilities were associated with higher clinical pregnancy and live birth rates. These results demonstrated the NICS-Timelapse model's significantly wider application in clinical IVF due to its excellent accuracy and noninvasiveness.
在体外受精(IVF)中选择尽可能优质的胚胎至关重要且具有革命性,尤其是当只移植一个胚胎以降低多胎妊娠的可能性时。然而,目前所使用方法的实际效用可能受到限制。在此,我们建立了一种新型的非侵入性胚胎评估方法,该方法将非侵入性染色体筛查(NICS)、延时成像系统以及人工智能算法相结合。NICS - 延时成像模型的曲线下面积(AUC)为0.94,准确率为0.88,能够预测囊胚整倍体情况。该模型的性能在75名处于不同临床情况的患者中进一步得到评估。NICS - 延时成像模型所预测胚胎的临床妊娠率和活产率表明,整倍体概率较高的胚胎与更高的临床妊娠率和活产率相关。这些结果证明,由于其卓越的准确性和非侵入性,NICS - 延时成像模型在临床IVF中具有显著更广泛的应用前景。