Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Department of Medicine, Harvard Medical School, Boston, MA, USA.
J Assist Reprod Genet. 2021 Oct;38(10):2663-2670. doi: 10.1007/s10815-021-02318-7. Epub 2021 Sep 17.
A deep learning artificial intelligence (AI) algorithm has been demonstrated to outperform embryologists in identifying euploid embryos destined to implant with an accuracy of 75.3% (1). Our aim was to evaluate the performance of highly trained embryologists in selecting top quality day 5 euploid blastocysts with and without the aid of a deep learning algorithm.
A non-overlapping series of 200 sets of day 5 euploid embryo images with known implantation outcomes was distributed to 17 highly trained embryologists. One embryo in each set was known to have implanted and one failed implantation. They were asked to select which embryo to transfer from each set. The same 200 sets of embryos, with indication of which embryo in each set had been identified by the algorithm as more likely to implant was then distributed. Chi-squared, t-test, and receiver operating curves were performed to compare the embryologist performeance with and without AI.
Fourteen embryologists completed both assessments. Embryologists provided with AI results selected successfully implanted embryos in 73.6% of cases compared to 65.5% for those selected using visual assessments alone (p < 0.001). All embryologists improved in their ability to select embryos with the aid of the AI algorithm with a mean percent improvement of 11.1% (range 1.4% to 15.5%). There were no differences in degree of improvement by embryologist level of experience (junior, intermediate, senior).
The incorporation of an AI framework for blastocyst selection enhanced the performance of trained embryologists in identifying PGT-A euploid embryos destined to implant.
深度学习人工智能（AI）算法已被证明在识别具有 75.3%准确率的可着床整倍体胚胎方面优于胚胎学家。我们的目的是评估在没有深度学习算法辅助的情况下，经过高度训练的胚胎学家选择优质第 5 天整倍体囊胚的表现。
将 200 组已知着床结果的第 5 天整倍体胚胎图像分配给 17 名经过高度训练的胚胎学家。每一组中的一个胚胎已知已着床，一个胚胎未着床。要求他们从每一组中选择要移植的胚胎。然后，将相同的 200 组胚胎分配给他们，每组胚胎都有指示，表明哪一个胚胎被算法识别为更有可能着床。进行卡方检验、t 检验和受试者工作特征曲线分析，以比较有和没有 AI 辅助时胚胎学家的表现。
14 名胚胎学家完成了两次评估。提供 AI 结果的胚胎学家在 73.6%的情况下选择了成功着床的胚胎，而仅通过视觉评估选择的胚胎为 65.5%（p<0.001）。所有胚胎学家在使用 AI 算法辅助选择胚胎方面的能力都有所提高，平均提高了 11.1%（范围为 1.4%至 15.5%）。胚胎学家的经验水平（初级、中级、高级）对提高程度没有差异。
胚胎选择中纳入 AI 框架增强了经过训练的胚胎学家识别具有 PGT-A 整倍体的胚胎着床的能力。
