Details

Autor(en) / Beteiligte

• Illingworth, Peter J
• Venetis, Christos
• Gardner, David K
• Nelson, Scott M
• Berntsen, Jørgen
• Larman, Mark G
• Agresta, Franca
• Ahitan, Saran
• Ahlström, Aisling
• Cattrall, Fleur
• Cooke, Simon
• Demmers, Kristy
• Gabrielsen, Anette
• Hindkjær, Johnny
• Kelley, Rebecca L
• Knight, Charlotte
• Lee, Lisa
• Lahoud, Robert
• Mangat, Manveen
• Park, Hannah
• Price, Anthony
• Trew, Geoffrey
• Troest, Bettina
• Vincent, Anna
• Wennerström, Susanne
• Zujovic, Lyndsey
• Hardarson, Thorir

Titel

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

Ist Teil von

• Nature medicine, 2024-08

Ort / Verlag

United States

Erscheinungsjahr

2024

Beschreibungen/Notizen

• 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 .