Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial.

ABSTRACT: To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Europe. Women under 42 years of age with at least two early-stage blastocysts on day 5 were randomized to either the control arm, using standard morphological assessment, or the study arm, employing a deep learning algorithm, intelligent Data Analysis Score (iDAScore), for embryo selection. The primary endpoint was a clinical pregnancy rate with a noninferiority margin of 5%. The trial included 1,066 patients (533 in the iDAScore group and 533 in the morphology group). The iDAScore group exhibited a clinical pregnancy rate of 46.5% (248 of 533 pati

SUBMITTER: Illingworth PJ 

PROVIDER: S-EPMC11564097 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature