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本文引用的文献

1
Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality.基于形态质量对人类胚胎图像进行分类的深度卷积神经网络评估。
Heliyon. 2021 Feb 23;7(2):e06298. doi: 10.1016/j.heliyon.2021.e06298. eCollection 2021 Feb.
2
The human factor: does the operator performing the embryo transfer significantly impact the cycle outcome?人为因素：胚胎移植操作者是否显著影响周期结局？
Hum Reprod. 2020 Feb 29;35(2):275-282. doi: 10.1093/humrep/dez290.
3
The impact of selected embryo culture conditions on ART treatment cycle outcomes: a UK national study.特定胚胎培养条件对辅助生殖技术治疗周期结局的影响：一项英国全国性研究。
Hum Reprod Open. 2020 Feb 10;2020(1):hoz031. doi: 10.1093/hropen/hoz031. eCollection 2020.
4
Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.开发和评估基于廉价自动化深度学习的胚胎学成像系统。
Lab Chip. 2019 Dec 21;19(24):4139-4145. doi: 10.1039/c9lc00721k. Epub 2019 Nov 22.
5
Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization.深度学习有助于在体外受精后对人类囊胚进行可靠的评估和筛选。
NPJ Digit Med. 2019 Apr 4;2:21. doi: 10.1038/s41746-019-0096-y. eCollection 2019.
6
An artificial neural network for the prediction of assisted reproduction outcome.人工神经网络在辅助生殖结局预测中的应用。
J Assist Reprod Genet. 2019 Jul;36(7):1441-1448. doi: 10.1007/s10815-019-01498-7. Epub 2019 Jun 19.
7
Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer.深度学习作为一种预测工具，用于在延时孵育和囊胚转移后预测妊娠的胎儿心脏。
Hum Reprod. 2019 Jun 4;34(6):1011-1018. doi: 10.1093/humrep/dez064.
8
Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018.人工智能和机器学习在人类生殖和胚胎学领域的应用：2018 年 ASRM 和 ESHRE 会议报告
J Assist Reprod Genet. 2019 Apr;36(4):591-600. doi: 10.1007/s10815-019-01408-x. Epub 2019 Jan 28.
9
Tracking quality: can embryology key performance indicators be used to identify clinically relevant shifts in pregnancy rate?跟踪质量：胚胎学关键绩效指标能否用于识别妊娠率的临床相关变化？
Hum Reprod. 2019 Jan 1;34(1):37-43. doi: 10.1093/humrep/dey349.
10
The Vienna consensus: report of an expert meeting on the development of ART laboratory performance indicators.维也纳共识：ART 实验室性能指标制定专家会议报告。
Reprod Biomed Online. 2017 Nov;35(5):494-510. doi: 10.1016/j.rbmo.2017.06.015. Epub 2017 Aug 4.

深度学习预警系统，用于评估辅助生殖技术实验室中的胚胎培养条件和胚胎学家的表现。

Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.

机构信息

Massachusetts General Hospital Fertility Center, Obstetrics/Gynecology/Reproductive Endocrinology and Infertility, Boston, MA, USA.

Colorado Center for Reproductive Medicine, Newport Beach, CA, USA.

出版信息

J Assist Reprod Genet. 2021 Jul;38(7):1641-1646. doi: 10.1007/s10815-021-02198-x. Epub 2021 Apr 27.

DOI:10.1007/s10815-021-02198-x
PMID:33904010
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8324654/
Abstract

Staff competency is a crucial component of the in vitro fertilization (IVF) laboratory quality management system because it impacts clinical outcomes and informs the key performance indicators (KPIs) used to continuously monitor and assess culture conditions. Contemporary quality control and assurance in the IVF lab can be automated (collect, store, retrieve, and analyze), to elevate quality control and assurance beyond the cursory monthly review. Here we demonstrate that statistical KPI monitoring systems for individual embryologist performance and culture conditions can be detected by artificial intelligence systems to provide systemic, early detection of adverse outcomes, and identify clinically relevant shifts in pregnancy rates, providing critical validation for two statistical process controls proposed in the Vienna Consensus Document; intracytoplasmic sperm injection (ICSI) fertilization rate and day 3 embryo quality.

摘要

员工能力是体外受精（IVF）实验室质量管理体系的关键组成部分，因为它会影响临床结果，并为用于持续监测和评估培养条件的关键绩效指标（KPI）提供信息。IVF 实验室的当代质量控制和保证可以实现自动化（收集、存储、检索和分析），从而将质量控制和保证提升到每月简单审查之上。在这里，我们证明了人工智能系统可以检测个别胚胎学家表现和培养条件的统计 KPI 监测系统，以提供系统的、早期的不良结果检测，并识别妊娠率的临床相关变化，为维也纳共识文件中提出的两个统计过程控制提供关键验证；胞浆内单精子注射（ICSI）受精率和第 3 天胚胎质量。