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

Journal: Journal of assisted reproduction and genetics
PMID: 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.

Authors

  • Charles L Bormann
    Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Carol Lynn Curchoe
    San Diego Fertility Center, 11425 El Camino Real, San Diego, CA, 92130, USA. carolcurchoe@32atps.com.
  • Prudhvi Thirumalaraju
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
  • Manoj K Kanakasabapathy
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Raghav Gupta
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
  • Rohan Pooniwala
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
  • Hemanth Kandula
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu.
  • Irene Souter
    Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Irene Dimitriadis
    Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynaecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Hadi Shafiee
    Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. hshafiee@bwh.harvard.edu and Department of Medicine, Harvard Medical School, Boston, MA, USA.

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