Portal de investigación
A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study.
Fecha de publicación:
Autores de IIS La Fe
Participantes ajenos a IIS La Fe
- Barnes, Josue
- Brendel, Matthew
- Gao, Vianne R.
- Rajendran, Suraj
- Kim, Junbum
- Li, Qianzi
- Malmsten, Jonas E.
- Sierra, Jose
- Zisimopoulos, Pantelis
- Sigaras, Alexandros
- Khosravi, Pegah
- Zhan, Qiansheng
- Rosenwaks, Zev
- Elemento, Olivier
- Zaninovic, Nikica
- Hajirasouliha, Iman
Grupos
Abstract
BACKGROUND: One challenge in the field of in-vitro fertilisation is the selection of the most viable embryos for transfer. Morphological quality assessment and morphokinetic analysis both have the disadvantage of intra-observer and inter-observer variability. A third method, preimplantation genetic testing for aneuploidy (PGT-A), has limitations too, including its invasiveness and cost. We hypothesised that differences in aneuploid and euploid embryos that allow for model-based classification are reflected in morphology, morphokinetics, and associated clinical information. METHODS: In this retrospective study, we used machine-learning and deep-learning approaches to develop STORK-A, a non-invasive and automated method of embryo evaluation that uses artificial intelligence to predict embryo ploidy status. Our method used a dataset of 10 378 embryos that consisted of static images captured at 110 h after intracytoplasmic sperm injection, morphokinetic parameters, blastocyst morphological assessments, maternal age, and ploidy status. Independent and external datasets, Weill Cornell Medicine EmbryoScope+ (WCM-ES+; Weill Cornell Medicine Center of Reproductive Medicine, NY, USA) and IVI Valencia (IVI Valencia, Health Research Institute la Fe, Valencia, Spain) were used to test the generalisability of STORK-A and were compared measuring accuracy and area under the receiver operating characteristic curve (AUC). FINDINGS: Analysis and model development included the use of 10 378 embryos, all with PGT-A results, from 1385 patients (maternal age range 21-48 years; mean age 36·98 years [SD 4·62]). STORK-A predicted aneuploid versus euploid embryos with an accuracy of 69·3% (95% CI 66·9-71·5; AUC 0·761; positive predictive value [PPV] 76·1%; negative predictive value [NPV] 62·1%) when using images, maternal age, morphokinetics, and blastocyst score. A second classification task trained to predict complex aneuploidy versus euploidy and single aneuploidy produced an accuracy of 74·0% (95% CI 71·7-76·1; AUC 0·760; PPV 54·9%; NPV 87·6%) using an image, maternal age, morphokinetic parameters, and blastocyst grade. A third classification task trained to predict complex aneuploidy versus euploidy had an accuracy of 77·6% (95% CI 75·0-80·0; AUC 0·847; PPV 76·7%; NPV 78·0%). STORK-A reported accuracies of 63·4% (AUC 0·702) on the WCM-ES+ dataset and 65·7% (AUC 0·715) on the IVI Valencia dataset, when using an image, maternal age, and morphokinetic parameters, similar to the STORK-A test dataset accuracy of 67·8% (AUC 0·737), showing generalisability. INTERPRETATION: As a proof of concept, STORK-A shows an ability to predict embryo ploidy in a non-invasive manner and shows future potential as a standardised supplementation to traditional methods of embryo selection and prioritisation for implantation or recommendation for PGT-A. FUNDING: US National Institutes of Health.
Copyright © 2023 Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.
Datos de la publicación
- ISSN/ISSNe:
- 2589-7500, 2589-7500
- Tipo:
- Article
- Páginas:
- 28-40
- PubMed:
- 36543475
- Enlace a otro recurso:
- www.scopus.com
- Factor de Impacto:
- 6,024 SCImago ℠
- Cuartil:
- Q1 SCImago ℠
Lancet Digital Health ELSEVIER
Citas Recibidas en Web of Science: 91
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Filiaciones
Keywords
- RISK CLASSIFICATION; HUMAN EMBRYOS; ANEUPLOIDY; SELECTION; EUPLOIDY; AGE