Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory
Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory
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DOI：
10.1007/s10815-021-02198-x
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发表时间：
2021-04-27
影响因子：
3.1
通讯作者：
Shafiee, Hadi
中科院分区：
文献类型：
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作者：
Curchoe, Carol Lynn;Thirumalaraju, Prudhvi;Shafiee, Hadi
工作人员能力是体外受精（IVF）实验室质量管理体系的重要组成部分，因为它影响临床结果，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实验室的现代质量控制和保证可以自动化（收集，存储，检索和分析），以提高质量控制和保证，而不仅仅是粗略的每月审查。在这里，我们证明了人工智能系统可以检测到个体胚胎学家表现和培养条件的统计KPI监测系统，以提供不良结果的系统性早期检测，并识别临床相关的妊娠率变化，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;卵胞浆内单精子注射（ICSI）受精率和第3天胚胎质量。
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.