Using a Disentangled Neural Network to Objectively Assess the Outcomes of Midfacial Surgery in Syndromic Craniosynostosis.

Journal: Plastic and reconstructive surgery
PMID: 39212945

Abstract

BACKGROUND: Advancements in artificial intelligence and the development of shape models that quantify normal head shape and facial morphology provide frameworks by which the outcomes of craniofacial surgery can be compared. In this work, the authors demonstrate the use of the swap disentangled variational autoencoder to assess changes after midfacial surgery objectively.

Authors

  • Alexander J Rickart
    UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
  • Simone Foti
    Department of Computing, Imperial College London.
  • Lara S van de Lande
    1 University College London (UCL) Great Ormond Street Institute of Child Health, London, UK.
  • Connor Wagner
    Division of Plastic, Reconstructive and Oral Surgery, Children's Hospital of Philadelphia.
  • Silvia Schievano
    UCL Great Ormond Street Institute of Child Health, London, UK.
  • Noor Ul Owase Jeelani
    From the UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children.
  • Matthew J Clarkson
    Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
  • Juling Ong
    UCL Great Ormond Street Institute of Child Health and Craniofacial Unit, Great Ormond Street Hospital for Children, London, UK.
  • Jordan W Swanson
    From the Divisions of Plastic, Reconstructive, and Oral Surgery.
  • Scott P Bartlett
    Division of Plastic, Reconstructive and Oral Surgery, Children's Hospital of Philadelphia.
  • Jesse A Taylor
    From the Divisions of Plastic, Reconstructive, and Oral Surgery.
  • David J Dunaway
    1 University College London (UCL) Great Ormond Street Institute of Child Health, London, UK.

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