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

• Published in: Journal of assisted reproduction and genetics Vol. 38; no. 7; pp. 1641 - 1646
• Main Authors: Bormann, Charles L., Curchoe, Carol Lynn, Thirumalaraju, Prudhvi, Kanakasabapathy, Manoj K., Gupta, Raghav, Pooniwala, Rohan, Kandula, Hemanth, Souter, Irene, Dimitriadis, Irene, Shafiee, Hadi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Artificial intelligence; Assisted Reproduction Technologies; Blastocyst - cytology; Blastocyst - physiology; Clinical decision making; Deep Learning; Early Warning Score; Embryo Culture Techniques - methods; Embryonic Development; Female; Gynecology; Human Genetics; Humans; Image Processing, Computer-Assisted - methods; In vitro fertilization; Laboratories; Laboratory Personnel - standards; Medicine; Medicine & Public Health; Neural Networks, Computer; Pregnancy; Pregnancy Rate; Process controls; Quality control; Reproductive Medicine; Sperm Injections, Intracytoplasmic - methods; Statistics; Proficiency; Quality assurance; Embryology; CNN; Assisted reproductive technologies; Convolutional neural network; Competency; Laboratory quality management systems; Infertility, Artificial intelligence; AI; Clinical decision-making; Embryo quality
• ISSN: 1058-0468; 1573-7330
• DOI: 10.1007/s10815-021-02198-x

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.