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.
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。
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本文引用的文献
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