Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial
Author(s)
Type
Journal Article
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 patients), compared to 48.2% (257 of 533 patients) in the morphology arm (risk difference −1.7%; 95% confidence interval −7.7, 4.3; P = 0.62). This study was not able to demonstrate noninferiority of deep learning for clinical pregnancy rate when compared to standard morphology and a predefined prioritization scheme. Australian New Zealand Clinical Trials Registry (ANZCTR) registration: 379161.
Date Issued
2024-11-01
Date Acceptance
2024-06-29
Citation
Nature Medicine, 2024, 30 (11), pp.3114-3120
ISSN
1078-8956
Publisher
Nature Research
Start Page
3114
End Page
3120
Journal / Book Title
Nature Medicine
Volume
30
Issue
11
Copyright Statement
© The Author(s) 2024 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39122964
PII: 10.1038/s41591-024-03166-5
Subjects
Biochemistry & Molecular Biology
BIRTH
BLASTOCYST TRANSFER
Cell Biology
IMAGES
Life Sciences & Biomedicine
Medicine, Research & Experimental
OUTCOMES
PERFORMANCE
PREDICTION
Research & Experimental Medicine
Science & Technology
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2024-08-09