| Literature DB >> 33904010 |
Charles L Bormann1, Carol Lynn Curchoe2, Prudhvi Thirumalaraju3, Manoj K Kanakasabapathy3, Raghav Gupta3, Rohan Pooniwala3, Hemanth Kandula3, Irene Souter1, Irene Dimitriadis1, Hadi Shafiee3.
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.Entities:
Keywords: AI; Assisted reproductive technologies; CNN; Clinical decision-making; Competency; Convolutional neural network; Embryo quality; Embryology; Infertility, Artificial intelligence; Laboratory quality management systems; Proficiency; Quality assurance
Mesh：
Year: 2021 PMID： 33904010 PMCID： PMC8324654 DOI： 10.1007/s10815-021-02198-x
Source DB: PubMed Journal: J Assist Reprod Genet ISSN： 1058-0468 Impact factor: 3.357