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Supported in part by Agencia Valenciana de la Innovacion (AVI) (2002-VLC-011-MM), by the Spanish Government (DIN2018-009911; E.P.), by the Generalitat Valenciana (AEST/2021/054; V.N.), and by the Generalitat Valenciana with the donation of the DGX A100 used for this work, action co-financed by the European Union through the Operational Program of the European Regional Development Fund of the Comunitat Valenciana 2014-2020 (IDIFEDER/2020/030).

Analysis of institutional authors

Payá, ElenaCorresponding AuthorPulgarín, CristianAuthorColomer, AdrianAuthorNaranjo, ValeryAuthor

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January 16, 2025
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Deep learning system for classification of ploidy status using time-lapse videos

Publicated to:F And S Science. 4 (3): 211-218 - 2023-08-01 4(3), DOI: 10.1016/j.xfss.2023.06.002

Authors: Paya, Elena; Pulgarin, Cristian; Bori, Lorena; Colomer, Adrian; Naranjo, Valery; Meseguer, Marcos

Affiliations

Hlth Res Inst Fe, Valencia, Spain - Author
IVIRMA Valencia, Valencia, Spain - Author
Univ Politecn Valencia, Inst Invest & Innovac Bioingn i3B, Valencia, Spain - Author

Abstract

Objective: To develop a spatiotemporal model for de prediction of euploid and aneuploid embryos using time-lapse videos from 10- Main Outcome Measures: The research used an end-to-end approach to develop an automated artificial intelligence system capable of extracting features from images and classifying them, considering spatiotemporal dependencies. A convolutional neural network extracted the most relevant features from each video frame. A bidirectional long short-term memory layer received this information and analyzed the temporal dependencies, obtaining a low-dimensional feature vector that characterized each video. A multilayer perceptron classified them into 2 groups, euploid and noneuploid. Results: The model performance in accuracy fell between 0.6170 and 0.7308. A multi-input model with a gate recurrent unit module performed better than others; the precision (or positive predictive value) is 0.8205 for predicting euploidy. Sensitivity, specificity, F1Score and accuracy are 0.6957, 0.7813, 0.7042, and 0.7308, respectively. Conclusions: This article proposes an artificial intelligence solution for prioritizing euploid embryo transfer. We can highlight the identification of a noninvasive method for chromosomal status diagnosis using a deep learning approach that analyzes raw data provided by time-lapse incubators. This method demonstrated potential automation of the evaluation process, allowing spatial and temporal information to encode. (Fertil Steril Sci (R) 2023;4:211-8. (c) 2023 by American Society for Reproductive Medicine.)

Keywords

AneuploidyArti fi cial intelligenceArtificial intelligenceBiopsyComputer visioComputer visionDeep learningEmbryosEuploidPgt-aPloidiesPloidyPredictionRemovaRetrospective studiesRetrospective studyTime lapse imagingTime-lapseTime-lapse imaging

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal F And S Science due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Embryology.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.15, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 4.64 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-18, the following number of citations:

  • WoS: 5
  • Scopus: 3
  • Europe PMC: 4

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-18:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 22 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

    Leadership analysis of institutional authors

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Paya Bosch, Elena) .

    the author responsible for correspondence tasks has been Paya Bosch, Elena.