Embryo selection with artificial intelligence: how to evaluate and compare methods?
Embryo selection with artificial intelligence: how to evaluate and compare methods?
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DOI：
10.1007/s10815-021-02254-6
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发表时间：
2021-07
影响因子：
3.1
通讯作者：
Karstoft H
中科院分区：
文献类型：
--
作者：
Kragh MF;Karstoft H
体外受精 (IVF) 中的胚胎选择是评估受精卵母细胞（胚胎）质量并选择患者群体中可用的最佳胚胎用于后续移植或冷冻保存的过程。近年来，人工智能（AI）已被广泛用于通过从胚胎显微镜图像中提取相关信息来改进和自动化胚胎排名和选择程序。人工智能模型的评估基于其识别成功怀孕机会最高的胚胎的能力。然而，这种评估是否应该基于排名表现或怀孕预测，似乎在研究中存在分歧。因此，报告了各种绩效指标，并且通常根据不同的结果和数据基础进行研究之间的比较。此外，人工智能方法相对于人工评估的优越性通常是基于回顾性数据而声称的，但没有提及潜在的偏见。在本文中，我们提供了一些主要主题的技术观点，这些主题划分了当前人工智能模型的训练、评估和比较方式。我们解释和讨论最常见的评估指标，并将它们与两个单独的评估目标（排名和预测）联系起来。我们还讨论了何时以及如何比较跨研究的人工智能模型，并详细解释了在回顾性队列研究中将人工智能模型与当前胚胎选择实践进行比较时，选择偏差是如何不可避免的。
Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.
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