Embryo selection through artificial intelligence versus embryologists: a systematic review.

IF 8.3 Q1 OBSTETRICS & GYNECOLOGY
Human reproduction open Pub Date : 2023-08-15 eCollection Date: 2023-01-01 DOI:10.1093/hropen/hoad031
M Salih, C Austin, R R Warty, C Tiktin, D L Rolnik, M Momeni, H Rezatofighi, S Reddy, V Smith, B Vollenhoven, F Horta
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引用次数: 2

Abstract

Study question: What is the present performance of artificial intelligence (AI) decision support during embryo selection compared to the standard embryo selection by embryologists?

Summary answer: AI consistently outperformed the clinical teams in all the studies focused on embryo morphology and clinical outcome prediction during embryo selection assessment.

What is known already: 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.

Study design size duration: 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.

Participants/materials setting 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.

Main results and the role of chance: 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%).

Limitations reasons for caution: 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.

Wider implications of the findings: 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.

Study funding/competing interests: This study was funded by Monash Data Future Institute. All authors have no conflicts of interest to declare.

Registration number: CRD42021256333.

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通过人工智能与胚胎学家进行胚胎选择:系统回顾。
研究问题:在胚胎选择过程中,与胚胎学家的标准胚胎选择相比,人工智能(AI)决策支持目前的表现如何?总结回答:在胚胎选择评估中,AI在所有以胚胎形态和临床结果预测为重点的研究中始终优于临床团队。已知情况:抗逆转录病毒治疗的成功率约为30%,令人担忧的趋势是,女性年龄的增长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一低成功率问题。随着人工智能的出现,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。鉴于人工智能在提高试管婴儿成功率方面的潜力,在胚胎选择过程中回顾人工智能和胚胎学家之间的表现仍然至关重要。研究设计规模持续时间:检索于2005年6月1日至2022年1月7日期间在PubMed、EMBASE、Ovid Medline和IEEE explore中完成。纳入的文章也仅限于用英文撰写的文章。该研究在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(“试管婴儿”或“体外受精*”或“辅助生殖技术*”或“胚胎”),其中字符“*”指的是搜索引擎,包括任何自动完成的搜索词。参与者/材料设置方法:检索人工智能在试管婴儿中的应用相关文献。主要研究结果是胚胎形态分级评估的准确性、敏感性和特异性,以及临床结果的可能性,如体外受精治疗后的临床妊娠。使用修改的Down和Black检查表评估偏倚风险。主要结果和偶然性的作用:本综述纳入了20篇文章。研究期间没有确定胚胎发育的具体评估日期,从胚胎发育第1天到第5/6天进行研究。训练AI算法的输入类型为图像加延时(10/20)、临床信息(6/20)、图像加临床信息(4/20)。与胚胎学家的视觉评估相比,每个人工智能模型都显示出了希望。平均而言,这些模型预测临床成功怀孕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。人工智能模型在预测胚胎形态等级方面的中位准确率为75.5%(范围为59-94%)。正确的预测(Ground Truth)是根据后胚胎学家的评估,根据当地各自的指导方针,通过使用胚胎图像来定义的。使用盲测数据集,胚胎学家的预测准确率为65.4%(范围47-75%),与原始的当地各自评估提供的基础真理相同。同样,人工智能模型通过使用患者临床治疗信息预测临床妊娠的中位准确率为77.8%(范围68-90%),而由胚胎学家进行预测的中位准确率为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为81.5%(范围为67-98%),而临床胚胎学家的中位数准确率为51%(范围为43-59%)。局限性:谨慎的原因:本综述的结果是基于尚未在临床环境中进行前瞻性评价的研究。此外,由于研究的异质性、人工智能模型的开发、使用的数据库以及研究的设计和质量,对所有研究进行公平比较被认为是不可行的。研究结果的更广泛含义:人工智能为体外受精领域和胚胎选择提供了相当大的希望。然而,开发人员对临床结果的看法需要转变,从成功植入到持续妊娠或活产。此外,现有模型主要关注本地生成的数据库,许多模型缺乏外部验证。研究经费/竞争利益:本研究由莫纳什数据未来研究所资助。所有作者无利益冲突需要声明。注册号:CRD42021256333。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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