Abstract：
This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring.
A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms.
Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916).
Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist.
Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias.
The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.
A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using key words and MeSH (Medical Subject Headings) terms.
Studies were included if they reported the accuracy of convolutional neural network models for embryo evaluation using time-lapse monitoring. The review was registered with PROSPERO (International Prospective Register of Systematic Reviews; identification number CRD42021275916).
Two reviewer authors independently screened results using the Covidence systematic review software. The full-text articles were reviewed when studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and the modified Joanna Briggs Institute or JBI checklist.
Following a systematic search of the literature, 22 studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization, blastocyst stage classification, and blastocyst quality. Most studies reported >80% accuracy, and embryologists were outperformed in some. Ten studies had a high risk of bias, mostly because of patient bias.
The application of artificial intelligence in time-lapse monitoring has the potential to provide more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, which heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among the included studies. Researchers should share databases and standardize reporting.
摘要：
目的：研究使用延时（TL）监视器评估胚胎的卷积神经网络（CNN）模型的准确性。
方法：于2016年1月至2022年12月在PubMed和WebofScience数据库中进行了系统搜索。通过使用关键字和MeSH术语来执行搜索策略。
方法：如果报道了使用TL评估胚胎的CNN模型的准确性，则包括研究。审查是在PROSPERO注册的，前瞻性国际系统评价登记册(识别号CRD42021275916)。
方法：两位评审作者使用Covidence系统评审软件独立筛选结果(VeritasHealthInnovation，墨尔本,澳大利亚)。当研究符合纳入标准或存在任何不确定性时，对全文进行了审查。未达成共识由第三位审阅者解决。使用QUADAS-2工具和修改后的JoannaBriggs研究所（JBI）清单评估偏倚和适用性的风险。
结果：在对文献进行系统搜索之后，22项研究被确定为符合纳入条件.所有研究均为回顾性研究。总共分析了222,998个胚胎的522,516个图像。评估了三个主要结局：成功的体外受精（IVF），囊胚分期分类,和胚泡质量，大多数研究报告准确率>80%，和一些优秀的胚胎学家。十项研究的偏倚风险很高，主要是由于患者的偏见。
结论：AI在TL监测仪中的应用具有更高的效率，准确,和客观的胚胎评价。检查胚泡阶段分类的模型显示出最佳预测。预测活产的模型有较低的偏倚风险，使用了最大的数据库，并进行了外部验证，提高了它们与临床应用的相关性。我们的系统评价受到研究之间高度异质性的限制。研究人员应共享数据库并进行标准化报告。
背景：这项研究没有从公众的资助机构获得任何特定的资助，商业,或非营利部门。
方法：于2016年1月至2022年12月在PubMed和WebofScience数据库中进行了系统搜索。通过使用关键字和MeSH术语来执行搜索策略。
方法：如果报道了使用TL评估胚胎的CNN模型的准确性，则包括研究。审查是在PROSPERO注册的，前瞻性国际系统评价登记册(识别号CRD42021275916)。
方法：两位评审作者使用Covidence系统评审软件独立筛选结果(VeritasHealthInnovation，墨尔本,澳大利亚)。当研究符合纳入标准或存在任何不确定性时，对全文进行了审查。未达成共识由第三位审阅者解决。使用QUADAS-2工具和修改后的JoannaBriggs研究所（JBI）清单评估偏倚和适用性的风险。
结果：在对文献进行系统搜索之后，22项研究被确定为符合纳入条件.所有研究均为回顾性研究。总共分析了222,998个胚胎的522,516个图像。评估了三个主要结局：成功的体外受精（IVF），囊胚分期分类,和胚泡质量，大多数研究报告准确率>80%，和一些优秀的胚胎学家。十项研究的偏倚风险很高，主要是由于患者的偏见。
结论：AI在TL监测仪中的应用具有更高的效率，准确,和客观的胚胎评价。检查胚泡阶段分类的模型显示出最佳预测。预测活产的模型有较低的偏倚风险，使用了最大的数据库，并进行了外部验证，提高了它们与临床应用的相关性。我们的系统评价受到研究之间高度异质性的限制。研究人员应共享数据库并进行标准化报告。
背景：这项研究没有从公众的资助机构获得任何特定的资助，商业,或非营利部门。
