Pacific In Vitro Fertilization Institute and John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
Pacific In Vitro Fertilization Institute and John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, Hawaii, USA.
Reprod Biomed Online. 2021 Jun;42(6):1075-1085. doi: 10.1016/j.rbmo.2021.02.015. Epub 2021 Mar 6.
Can artificial intelligence (AI) discriminate a blastocyst's cellular area from unedited time-lapse image files using semantic segmentation and a deep learning optimized U-Net architecture for use in selecting single blastocysts for transfer?
This platform was retrospectively applied to time-lapse files from 101 sequentially transferred single blastocysts that were prospectively selected for transfer by their highest expansion ranking within cohorts using a 10 h expansion assay rather than standard grading.
The AI platform provides expansion curves and raw data files to classify and compare blastocyst phenotypes within both cohorts and populations. Of 35 sequential unbiopsied single blastocyst transfers, 23 (65.7%) resulted in a live birth. Of 66 sequential single euploid blastocyst transfers, also selected for their most robust expansion, 49 (74.2%) resulted in live birth. The AI platform revealed that the averaged expansion rate was significantly (P = 0.007) greater in euploid blastocysts that resulted in live births compared with those resulting in failure to give a live birth. The platform further provides a framework to analyse fragmentation phenotypes that can test new hypotheses for developmental regulation during the preimplantation period.
AI can be used to quantitatively describe blastocyst expansion from unedited time-lapse image files and can be used to quantitatively rank-order blastocysts for transfer. Early clinical results from such single blastocyst selection suggests that live birth rates without biopsy may be comparable to those found using single euploid blastocysts in younger, good responder patients.
人工智能（AI）是否可以使用语义分割和经过深度学习优化的 U-Net 架构从未编辑的延时图像文件中区分胚胎的细胞区域，以便用于选择单个胚胎进行转移？
该平台回顾性地应用于 101 个连续转移的单个胚胎的延时文件，这些胚胎是通过使用 10 小时扩展测定法而非标准分级对队列内的最高扩展排名进行前瞻性选择用于转移的。
AI 平台提供了扩展曲线和原始数据文件，用于在两个队列和人群内对胚胎表型进行分类和比较。在 35 个连续的未经活检的单个胚胎转移中，有 23 个（65.7%）导致活产。在 66 个连续的单个整倍体胚胎转移中，也选择了最稳健的扩展胚胎，其中 49 个（74.2%）导致活产。AI 平台表明，在导致活产的整倍体胚胎中，平均扩展率明显更高（P=0.007），而在导致无活产的胚胎中则更低。该平台还提供了一个分析碎片表型的框架，可以测试胚胎植入前期间发育调节的新假设。
AI 可用于从未编辑的延时图像文件中定量描述胚胎的扩展，并可用于对胚胎进行定量排序以进行转移。这种单个胚胎选择的早期临床结果表明，在没有活检的情况下，活产率可能与在年轻、反应良好的患者中使用单个整倍体胚胎的活产率相当。
