Objective

To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the secondary objective was to identify limitations that may impact clinical use.

Design

Retrospective study.

Setting

Consortium of 11 assisted reproductive technology centers in the United States.

Patient(s)

Static images of 5,923 transferred blastocysts and 2,614 nontransferred aneuploid blastocysts.

Intervention(s)

None.

Main Outcome Measure(s)

Prediction of clinical pregnancy (fetal heartbeat).

Result(s)

The area under the curve of the AI model ranged from 0.6 to 0.7 and outperformed manual morphology grading overall and on a per-site basis. A bootstrapped study predicted improved pregnancy rates between +5% and +12% per site using AI compared with manual grading using an inverted microscope. One site that used a low-magnification stereo zoom microscope did not show predicted improvement with the AI. Visualization techniques and attribution algorithms revealed that the features learned by the AI model largely overlap with the features of manual grading systems. Two sources of bias relating to the type of microscope and presence of embryo holding micropipettes were identified and mitigated. The analysis of AI scores in relation to pregnancy rates showed that score differences of ≥0.1 (10%) correspond with improved pregnancy rates, whereas score differences of <0.1 may not be clinically meaningful.

Conclusion(s)

This study demonstrates the potential of AI for ranking blastocyst stage embryos and highlights potential limitations related to image quality, bias, and granularity of scores.

中文翻译：

---

用于对囊胚期胚胎静态图像进行排序的人工智能模型的表征

客观的

执行一系列表征人工智能 (AI) 模型的分析，以对胚泡期胚胎进行排序。主要目标是评估该模型预测临床妊娠的益处，而次要目标是确定可能影响临床使用的局限性。

设计

回顾性研究。

环境

美国 11 个辅助生殖技术中心联盟。

耐心）

5,923 个转移的囊胚和 2,614 个非转移的非整倍体囊胚的静态图像。

干预措施

没有。

主要观察指标）

临床妊娠预测（胎心）。

结果）

AI 模型的曲线下面积在 0.6 到 0.7 之间，整体上和每个站点的性能都优于手动形态分级。一项自举研究预测，与使用倒置显微镜进行手动分级相比，使用 AI 可将每个部位的妊娠率提高 +5% 至 +12%。一个使用低倍率立体变焦显微镜的站点没有显示出人工智能的预期改进。可视化技术和归因算法表明，人工智能模型学习的特征在很大程度上与手动评分系统的特征重叠。确定并减轻了与显微镜类型和胚胎保持微量移液器存在相关的两个偏差来源。与妊娠率相关的 AI 评分分析表明，≥0.1 (10%) 的评分差异对应于妊娠率的提高，

结论

这项研究展示了人工智能对囊胚期胚胎进行排名的潜力，并强调了与图像质量、偏差和分数粒度相关的潜在限制。