Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth
Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth
复制标题
DOI：
10.1016/j.rbmo.2021.05.002
复制
发表时间：
2021-11-08
影响因子：
4
通讯作者：
Sugiura-Ogasawara, Mayumi
中科院分区：
文献类型：
--
作者：
Sawada, Yuki;Sato, Takeshi;Sugiura-Ogasawara, Mayumi
研究问题：人工智能(AI)能否提高基于胚胎图像的活产预测？设计：应用与深度学习相关的注意分支网络建立人工智能系统，从470个移植胚胎的141,444个图像中预测活产的概率，其中91个导致活产，379个导致非活产，包括植入失败、生化妊娠和临床流产。研究人员检查了计算出的每个胚胎的置信度分数和每个胚胎图像中显示的聚焦区域是否可以帮助预测后续的活产。结果：人工智能系统首次成功地将胚胎特征可视化到有可能区分活产和非活产的重点区域。尽管有许多图像显示透明带周围存在高聚焦区，但没有观察到与活产或非活产有关的胚胎的视觉特征。当置信度评分的临界值设置为0.341时，得分高于临界值的胚胎的活产率显著高于低于临界值的胚胎(P<；0.001)。此外，形态质量和置信度评分高于0.341的胚胎活产率为41.1%。结论：作者已经创建了一个具有置信度分数的人工智能系统，该系统有助于对可能导致活产的胚胎进行非侵入性选择。有必要进一步研究，以提高选择的准确性。
Research question: Can artificial intelligence (AI) improve the prediction of live births based on embryo images? Design: The AI system was created by using the Attention Branch Network associated with deep learning to predict the probability of live birth from 141,444 images recorded by time-lapse imaging of 470 transferred embryos, of which 91 resulted in live birth and 379 resulted in non-live birth that included implantation failure, biochemical pregnancy and clinical miscarriage. The possibility that the calculated confidence scores of each embryo and the focused areas visualized in each embryo image can help predict subsequent live birth was examined. Results: The AI system for the first time successfully visualized embryo features in focused areas that had potential to distinguish between live and non-live births. No visual feature of embryos were visualized that were associated with live or non-live births, although there were many images in which high-focused areas existed around the zona pellucida. When a cut-off level for the confidence score was set at 0.341, the live birth rate was significantly greater for embryos with a score higher than the cut-off level than for those with a score lower than the cut-off level (P < 0.001). In addition, the live birth rate of embryos with good morphological quality and confidence scores higher than 0.341 was 41.1%. Conclusions: The authors have created an AI system with a confidence score that is useful for non-invasive selection of embryos that could result in live birth. Further study is necessary to improve selection accuracy.