Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory.
J Assist Reprod Genet
; 38(7): 1641-1646, 2021 Jul.
Article
en En
| MEDLINE
| ID: mdl-33904010
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Inyecciones de Esperma Intracitoplasmáticas
/
Técnicas de Cultivo de Embriones
/
Personal de Laboratorio
/
Aprendizaje Profundo
/
Puntuación de Alerta Temprana
Tipo de estudio:
Prognostic_studies
/
Screening_studies
Límite:
Female
/
Humans
/
Pregnancy
Idioma:
En
Revista:
J Assist Reprod Genet
Asunto de la revista:
GENETICA
/
MEDICINA REPRODUTIVA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos