Quantifying dysmorphologies of the neurocranium using artificial neural networks.

Journal: Journal of anatomy
PMID: 38760946

Abstract

BACKGROUND: Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for evaluating cranial morphology to enhance patient treatment. Current subjective assessments often lead to inconsistent outcomes. This study introduces a novel, quantitative approach to classify craniosynostosis and measure its severity.

Authors

  • Tareq Abdel-Alim
    Department of Neurosurgery, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Franz Tapia Chaca
    Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Irene M J Mathijssen
    Department of Plastic and Reconstructive Surgery, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Clemens M F Dirven
    Department of Neurosurgery, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Wiro J Niessen
    Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Eppo B Wolvius
    Department of Oral- and Maxillofacial Surgery, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Marie-Lise C van Veelen
    Department of Neurosurgery, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Gennady V Roshchupkin
    Department of Radiology and Nuclear Medicine, Erasmus MC, Medical Center, Rotterdam, the Netherlands. g.roshchupkin@erasmusmc.nl.

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