A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development.

Journal: Journal of ovarian research
PMID: 38491534

Abstract

BACKGROUND: Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5.

Authors

  • S Canosa
    Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy. s.canosa88@gmail.com.
  • N Licheri
    Department of Computer Science, University di Turin, Turin, Italy.
  • L Bergandi
    Department of Oncology, University of Turin, Turin, Italy.
  • G Gennarelli
    Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy.
  • C Paschero
    Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy.
  • M Beccuti
    Department of Computer Science, University di Turin, Turin, Italy.
  • D Cimadomo
    IVIRMA Global Research Alliance, Genera, Clinica Valle Giulia, Rome, Italy.
  • G Coticchio
    IVIRMA Global Research Alliance, 9.Baby, Bologna, Italy.
  • L Rienzi
    IVIRMA Global Research Alliance, Genera, Clinica Valle Giulia, Rome, Italy.
  • C Benedetto
    Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy.
  • F Cordero
    Department of Computer Science, University di Turin, Turin, Italy.
  • A Revelli
    Gynecology and Obstetrics 1U, Physiopathology of Reproduction and IVF Unit, Department of Surgical Sciences, S. Anna Hospital, University of Turin, Turin, Italy.

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