目的:与胚胎学家的标准胚胎选择相比,人工智能(AI)决策支持在胚胎选择过程中的表现如何?
结论:在所有专注于胚胎形态和胚胎选择评估的临床结果预测的研究中,AI始终优于临床团队。
背景:ART成功率为30%,令人担忧的女性年龄增长趋势与相当糟糕的结果相关。因此,一直在努力通过开发新技术来解决这种低成功率。随着AI的出现,机器学习有可能以这样一种方式应用,即受人类主观性限制的领域,如胚胎选择,可以通过增加客观性来增强。考虑到人工智能提高试管婴儿成功率的潜力,评估AI和胚胎学家在胚胎选择过程中的表现仍然至关重要.
■搜索是在PubMed中进行的,EMBASE,OvidMedline,和IEEEXplore自2005年6月1日起至2022年1月7日止。包含的文章也仅限于用英语写的文章。在研究的所有数据库中使用的搜索词是:(\'人工智能\'或\'机器学习\'或\'深度学习\'或\'神经网络\')和(\'IVF\'或\'体外受孕*\'或\'辅助生殖技术*\'或\'胚胎\'),其中字符\'*\'是指搜索引擎包含搜索词的任何自动完成。
方法:对与AI应用于IVF相关的文献进行了文献检索。感兴趣的主要结果是准确性,灵敏度,胚胎形态分级评估的特异性和临床结果的可能性,如IVF治疗后的临床妊娠。使用改良的Down和Black检查表评估偏倚风险。
结果:本综述包括20篇文章。在整个研究中没有特定的胚胎评估日-研究胚胎发育的第1天直到第5/6天。训练AI算法的输入类型是图像和延时(10/20),临床信息(6/20),以及图像和临床信息(4/20)。与胚胎学家的视觉评估相比,每个AI模型都表现出了希望。平均而言,这些模型预测成功临床妊娠的可能性比临床胚胎学家更准确,与人类预测相比,这意味着更高的可靠性。AI模型在预测胚胎形态等级方面的中值准确度为75.5%(范围59-94%)。正确的预测(GroundTruth)是根据胚胎学家根据当地各自的指南进行的评估,通过使用胚胎图像来定义的。使用盲测试数据集,胚胎学家的预测准确率为65.4%(范围47-75%),与最初的当地评估提供的基本事实相同.同样,AI模型通过使用患者临床治疗信息预测临床妊娠的平均准确率为77.8%(范围68-90%),而胚胎学家则为64%(范围58-76%)。当图像/延时和临床信息输入相结合时,人工智能模型的中位数准确率更高,为81.5%(范围67-98%),而临床胚胎学家的中位准确率为51%(范围43-59%).
结论:本综述的发现是基于尚未在临床环境中进行前瞻性评估的研究。此外,由于研究的异质性,所有研究的公平比较被认为是不可行的,人工智能模型的发展,采用的数据库以及研究设计和质量。
结论:AI为IVF领域和胚胎选择提供了可观的前景。然而,开发人员对临床结局的看法需要从成功植入转向持续妊娠或活产。此外,现有的模型集中在本地生成的数据库,许多缺乏外部验证。
背景:这项研究由莫纳什数据未来研究所资助。所有作者都没有利益冲突要声明。
背景:CRD42021256333。
OBJECTIVE: What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists?
CONCLUSIONS: AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment.
BACKGROUND: The ART success rate is ∼30%, with a worrying trend of increasing female age correlating with considerably worse results. As such, there have been ongoing efforts to address this low success rate through the development of new technologies. With the advent of AI, there is potential for machine learning to be applied in such a manner that areas limited by human subjectivity, such as embryo selection, can be enhanced through increased objectivity. Given the potential of AI to improve IVF success rates, it remains crucial to
review the performance between AI and embryologists during embryo selection.
UNASSIGNED: The search was done across PubMed, EMBASE, Ovid Medline, and IEEE Xplore from 1 June 2005 up to and including 7 January 2022. Included articles were also restricted to those written in English. Search terms utilized across all databases for the study were: (\'Artificial intelligence\' OR \'Machine Learning\' OR \'Deep learning\' OR \'Neural network\') AND (\'IVF\' OR \'in vitro fertili*\' OR \'assisted reproductive techn*\' OR \'embryo\'), where the character \'*\' refers the search engine to include any auto completion of the search term.
METHODS: A literature search was conducted for literature relating to AI applications to IVF. Primary outcomes of interest were accuracy, sensitivity, and specificity of the embryo morphology grade assessments and the likelihood of clinical outcomes, such as clinical pregnancy after IVF treatments. Risk of bias was assessed using the Modified Down and Black Checklist.
RESULTS: Twenty articles were included in this
review. There was no specific embryo assessment day across the studies-Day 1 until Day 5/6 of embryo development was investigated. The types of input for training AI algorithms were images and time-lapse (10/20), clinical information (6/20), and both images and clinical information (4/20). Each AI model demonstrated promise when compared to an embryologist\'s visual assessment. On average, the models predicted the likelihood of successful clinical pregnancy with greater accuracy than clinical embryologists, signifying greater reliability when compared to human prediction. The AI models performed at a median accuracy of 75.5% (range 59-94%) on predicting embryo morphology grade. The correct prediction (Ground Truth) was defined through the use of embryo images according to post embryologists\' assessment following local respective guidelines. Using blind test datasets, the embryologists\' accuracy prediction was 65.4% (range 47-75%) with the same ground truth provided by the original local respective assessment. Similarly, AI models had a median accuracy of 77.8% (range 68-90%) in predicting clinical pregnancy through the use of patient clinical treatment information compared to 64% (range 58-76%) when performed by embryologists. When both images/time-lapse and clinical information inputs were combined, the median accuracy by the AI models was higher at 81.5% (range 67-98%), while clinical embryologists had a median accuracy of 51% (range 43-59%).
CONCLUSIONS: The findings of this
review are based on studies that have not been prospectively evaluated in a clinical setting. Additionally, a fair comparison of all the studies were deemed unfeasible owing to the heterogeneity of the studies, development of the AI models, database employed and the study design and quality.
CONCLUSIONS: AI provides considerable promise to the IVF field and embryo selection. However, there needs to be a shift in developers\' perception of the clinical outcome from successful implantation towards ongoing pregnancy or live birth. Additionally, existing models focus on locally generated databases and many lack external validation.
BACKGROUND: This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare.
BACKGROUND: CRD42021256333.