Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation.
Journal:
Reproductive biology and endocrinology : RB&E
PMID:
39891250
Abstract
BACKGROUND: Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency and reliability in clinical settings during its development and validation process. We present a methodology for developing and validating an AI model across multiple datasets to demonstrate reliable performance in evaluating blastocyst-stage embryos.