Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.

Journal: International journal of computer assisted radiology and surgery
PMID: 39207718

Abstract

PURPOSE: Skull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging studies and is time consuming. The goal of this study was to develop a fully automated deep learning method for FO segmentation and to determine the predictive value in identifying patients with sCSF leak or encephalocele.

Authors

  • Steven D Curry
    Department of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA. scurry@houseclinic.com.
  • Kieran S Boochoon
    Department of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA.
  • Geoffrey C Casazza
    Department of Otolaryngology, Head and Neck Surgery, University of Nebraska Medical Center, 981225 Nebraska Medical Center, Omaha, NE, 68198-1225, USA.
  • Daniel L Surdell
    Department of Neurosurgery, University of Nebraska Medical Center, 988437 Nebraska Medical Center, Omaha, NE, 68198-8437, USA.
  • Justin A Cramer
    University of Nebraska Medical Center, Omaha, NE, USA.

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