Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation.

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.

Methods

This multicenter analysis utilizes time-lapse images, pregnancy outcomes, and morphologic annotations from embryos collected at 10 IVF clinics across 9 countries between 2018 and 2022. The four-step methodology for developing and evaluating the AI model include: (I) curating

SUBMITTER: Gilboa D 

PROVIDER: S-EPMC11783712 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature