Clinical outcomes of single blastocyst transfer with machine learning guided noninvasive chromosome screening grading system in infertile patients.

IF 4.2 2区 医学 Q1 ENDOCRINOLOGY & METABOLISM
Xiaoxi Li, Yaxin Yao, Dunmei Zhao, Xiufeng Chang, Yi Li, Huilan Lin, Huijuan Wei, Haiye Wang, Ying Mi, Lei Huang, Sijia Lu, Weimin Yang, Liyi Cai
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Abstract

Background: Prospective observational studies have demonstrated that the machine learning (ML) -guided noninvasive chromosome screening (NICS) grading system, which we called the noninvasive chromosome screening-artificial intelligence (NICS-AI) grading system, can be used embryo selection. The current prospective interventional clinical study was conducted to investigate whether this NICS-AI grading system can be used as a powerful tool for embryo selection.

Methods: Patients who visited our centre between October 2018 and December 2021 were recruited. Grade A and B embryos with a high probability of euploidy were transferred in the NICS group. The patients in the control group selected the embryos according to the traditional morphological grading. Finally, 90 patients in the NICS group and 161 patients in the control group were compared statistically for their clinical outcomes.

Results: In the NICS group, the clinical pregnancy rate (70.0% vs. 54.0%, p < 0.001), the ongoing pregnancy rate (58.9% vs. 44.7%, p = 0.001), and the live birth rate (56.7% vs. 42.9%, p = 0.001) were significantly higher than those of the control group. When the female was ≥ 35 years old, the clinical pregnancy rate (67.7% vs. 32.1%, p < 0.001), ongoing pregnancy rate (56.5% vs. 25.0%, p = 0.001), and live birth rate (54.8% vs. 25.0%, p = 0.001) in the NICS group were significantly higher than those of the control group. Regardless of whether the patients had a previous record of early spontaneous abortion or not, the live birth rate of the NICS group was higher than that of the control group (61.0% vs. 46.9%; 57.9% vs. 34.8%; 33.3% vs. 0%) but the differences were not statistically significant.

Conclusions: NICS-AI was able to improve embryo utilisation rate, and the live birth rate, especially for those ≥ 35 years old, with transfer of Grade A embryos being preferred, followed by Grade B embryos. NICS-AI can be used as an effective tool for embryo selection in the future.

不孕症患者使用机器学习引导的无创染色体筛查分级系统进行单囊胚移植的临床效果。
背景:前瞻性观察研究表明,机器学习(ML)指导下的无创染色体筛查(NICS)分级系统(我们称之为无创染色体筛查-人工智能(NICS-AI)分级系统)可用于胚胎选择。本次前瞻性介入临床研究旨在探究该NICS-AI分级系统能否作为胚胎选择的有力工具:招募2018年10月至2021年12月期间到我中心就诊的患者。在 NICS 组中移植了极易发生非整倍体的 A 级和 B 级胚胎。对照组患者根据传统的形态学分级选择胚胎。最后,对 NICS 组的 90 名患者和对照组的 161 名患者的临床结果进行统计比较:结果:NICS 组的临床妊娠率(70.0% 对 54.0%,PNICS-AI能够提高胚胎利用率和活产率,尤其是对于年龄≥35岁的患者,A级胚胎是首选,其次是B级胚胎。未来,NICS-AI 可作为胚胎选择的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Reproductive Biology and Endocrinology
Reproductive Biology and Endocrinology 医学-内分泌学与代谢
CiteScore
7.90
自引率
2.30%
发文量
161
审稿时长
4-8 weeks
期刊介绍: Reproductive Biology and Endocrinology publishes and disseminates high-quality results from excellent research in the reproductive sciences. The journal publishes on topics covering gametogenesis, fertilization, early embryonic development, embryo-uterus interaction, reproductive development, pregnancy, uterine biology, endocrinology of reproduction, control of reproduction, reproductive immunology, neuroendocrinology, and veterinary and human reproductive medicine, including all vertebrate species.
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