Head-to-head comparison of six commercial artificial intelligence algorithms for embryo ploidy risk stratification in 1000 PGT-A-tested blastocysts

Autores de IIS La Fe

• Carla Giménez Rodríguez

  Autor

• Lorena Bori Arnal

  Autor

• José Bellver Pradas

  Autor

• José Remohí Giménez

  Autor

• 

  Marcos Meseguer Escrivá

  Autor

Participantes ajenos a IIS La Fe

• Caparrós López-Battú B.

Grupos

• Biomarcadores, Medicina Genómica, Estadística y Análisis masivo de datos en Reproducción humana asistida

• Endocrinología Reproductiva e Infertilidad

• Medicina Fetal y Reproductiva

Abstract

Research question: Do commercial artificial intelligence (AI)-based embryo assessment algorithms differ in their ability to stratify the risk of embryo ploidy, or do they converge towards a similar level of discrimination despite being developed for different clinical objectives? Design: Retrospective cohort study conducted at a single IVF centre, including 1000 blastocysts from 273 patients treated between 2022 and 2023. Embryos were scored using six anonymized commercial AI models (three each of static- or video-based). Ploidy status was determined by trophectoderm biopsy followed by preimplantation genetic testing for aneuploidy (PGT-A). Performance was evaluated using multivariable logistic regression and receiver operating characteristic curves (continuous scores). Euploidy enrichment across within-algorithm score strata and rank concordance (Kendall’s t-b) were assessed, with subgroup analyses by Asociación para el Estudio de la Biología de la Reproducción morphological grade and oocyte age. Results: All six algorithms showed moderate discrimination between euploid and aneuploid embryos (area under the curve range 0.713–0.744, overlapping 95% CI), and their continuous scores were significantly associated with ploidy status (all P < 0.001). For each model, higher score strata were associated with progressive enrichment of euploid embryos, despite substantial overlap in score distributions between ploidy groups. Ranking concordance between algorithms was moderate, indicating that embryos prioritized by different systems frequently differed. These patterns were broadly consistent across morphological grades and maternal age strata. Conclusions: Across 1000 PGT-A-validated blastocysts, commercial AI embryo assessment algorithms converge towards a shared ceiling of ploidy risk stratification performance. These tools should therefore be interpreted as relative risk stratification systems rather than diagnostic tests. While AI scores may support embryo prioritization when PGT-A cannot be performed, limited concordance across algorithms indicates that the choice of algorithm can influence which embryos are prioritized. © 2026 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Copyright © 2026 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Keywords

• Artificial intelligence; Embryo ploidy; Machine learning; Preimplantation genetic testing for aneuploidy; Risk stratification