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Updated: Jun 25, 2025
Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
Published on: August 25, 2019
Clinical outcomes of single blastocyst transfer with machine learning guided noninvasive chromosome screening grading
Xiaoxi Li1, Yaxin Yao2, Dunmei Zhao2
1Reproductive Medicine Department of Hebei Maternity Hospital, Shijiazhuang, China.
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.
