Deep Learning Versus Manual Morphology-based Embryo Selection in IVF: a Randomized, Double-blind Noninferiority Trial

Overview

Journal Nat Med

Specialties General Medicine
Molecular Biology

Date 2024 Aug 9

PMID 39122964

Authors

Peter J Illingworth

Christos Venetis

David K Gardner

Scott M Nelson

Jorgen Berntsen

Mark G Larman

Franca Agresta

Saran Ahitan

Aisling Ahlstrom

Fleur Cattrall

Simon Cooke

Kristy Demmers

Anette Gabrielsen

Johnny Hindkjaer

Rebecca L Kelley

Charlotte Knight

Lisa Lee

Robert Lahoud

Manveen Mangat

Hannah Park

Anthony Price

Geoffrey Trew

Bettina Troest

Anna Vincent

Susanne Wennerstrom

Lyndsey Zujovic

Thorir Hardarson

Affiliations

Soon will be listed here.

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 .

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References

Kragh M, Rimestad J, Berntsen J, Karstoft H . Automatic grading of human blastocysts from time-lapse imaging. Comput Biol Med. 2019; 115:103494. DOI: 10.1016/j.compbiomed.2019.103494. View

Cimadomo D, Chiappetta V, Innocenti F, Saturno G, Taggi M, Marconetto A . Towards Automation in IVF: Pre-Clinical Validation of a Deep Learning-Based Embryo Grading System during PGT-A Cycles. J Clin Med. 2023; 12(5). PMC: 10002983. DOI: 10.3390/jcm12051806. View

Afnan M, Liu Y, Conitzer V, Rudin C, Mishra A, Savulescu J . Interpretable, not black-box, artificial intelligence should be used for embryo selection. Hum Reprod Open. 2021; 2021(4):hoab040. PMC: 8687137. DOI: 10.1093/hropen/hoab040. View

Miyagi Y, Habara T, Hirata R, Hayashi N . Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age. Reprod Med Biol. 2019; 18(2):190-203. PMC: 6452012. DOI: 10.1002/rmb2.12266. View

Sawada Y, Sato T, Nagaya M, Saito C, Yoshihara H, Banno C . Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth. Reprod Biomed Online. 2021; 43(5):843-852. DOI: 10.1016/j.rbmo.2021.05.002. View

McCambridge J, Witton J, Elbourne D . Systematic review of the Hawthorne effect: new concepts are needed to study research participation effects. J Clin Epidemiol. 2013; 67(3):267-77. PMC: 3969247. DOI: 10.1016/j.jclinepi.2013.08.015. View

Storr A, Venetis C, Cooke S, Kilani S, Ledger W . Inter-observer and intra-observer agreement between embryologists during selection of a single Day 5 embryo for transfer: a multicenter study. Hum Reprod. 2016; 32(2):307-314. DOI: 10.1093/humrep/dew330. View

Hernan M, Robins J . Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. Am J Epidemiol. 2016; 183(8):758-64. PMC: 4832051. DOI: 10.1093/aje/kwv254. View

De Geyter C, Wyns C, Calhaz-Jorge C, de Mouzon J, Ferraretti A, Kupka M . 20 years of the European IVF-monitoring Consortium registry: what have we learned? A comparison with registries from two other regions. Hum Reprod. 2020; 35(12):2832-2849. PMC: 7744162. DOI: 10.1093/humrep/deaa250. View

10.

Dirvanauskas D, Maskeliunas R, Raudonis V, Damasevicius R . Embryo development stage prediction algorithm for automated time lapse incubators. Comput Methods Programs Biomed. 2019; 177:161-174. DOI: 10.1016/j.cmpb.2019.05.027. View

11.

Alikani M, Go K, McCaffrey C, McCulloh D . Comprehensive evaluation of contemporary assisted reproduction technology laboratory operations to determine staffing levels that promote patient safety and quality care. Fertil Steril. 2014; 102(5):1350-6. DOI: 10.1016/j.fertnstert.2014.07.1246. View

12.

Lang K, Josefsson V, Larsson A, Larsson S, Hogberg C, Sartor H . Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded,.... Lancet Oncol. 2023; 24(8):936-944. DOI: 10.1016/S1470-2045(23)00298-X. View

13.

Benchaib M, Labrune E, Giscard dEstaing S, Salle B, Lornage J . Shallow artificial networks with morphokinetic time-lapse parameters coupled to ART data allow to predict live birth. Reprod Med Biol. 2022; 21(1):e12486. PMC: 9601773. DOI: 10.1002/rmb2.12486. View

14.

Jiang V, Bormann C . Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade. Fertil Steril. 2023; 120(1):17-23. DOI: 10.1016/j.fertnstert.2023.05.149. View

15.

Paya E, Bori L, Colomer A, Meseguer M, Naranjo V . Automatic characterization of human embryos at day 4 post-insemination from time-lapse imaging using supervised contrastive learning and inductive transfer learning techniques. Comput Methods Programs Biomed. 2022; 221:106895. DOI: 10.1016/j.cmpb.2022.106895. View

16.

Ahlstrom A, Lundin K, Lind A, Gunnarsson K, Westlander G, Park H . A double-blind randomized controlled trial investigating a time-lapse algorithm for selecting Day 5 blastocysts for transfer. Hum Reprod. 2022; 37(4):708-717. PMC: 9383441. DOI: 10.1093/humrep/deac020. View

17.

Tran D, Cooke S, Illingworth P, Gardner D . Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod. 2019; 34(6):1011-1018. PMC: 6554189. DOI: 10.1093/humrep/dez064. View

18.

Kieslinger D, Vergouw C, Ramos L, Arends B, Curfs M, Slappendel E . Clinical outcomes of uninterrupted embryo culture with or without time-lapse-based embryo selection versus interrupted standard culture (SelecTIMO): a three-armed, multicentre, double-blind, randomised controlled trial. Lancet. 2023; 401(10386):1438-1446. DOI: 10.1016/S0140-6736(23)00168-X. View

19.

DAgostino Sr R, Massaro J, Sullivan L . Non-inferiority trials: design concepts and issues - the encounters of academic consultants in statistics. Stat Med. 2003; 22(2):169-86. DOI: 10.1002/sim.1425. View

20.

Wang S, Chen L, Sun H . Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study. Reprod Biomed Online. 2023; 47(6):103371. DOI: 10.1016/j.rbmo.2023.103371. View