A preliminary review of the utility of artificial intelligence to detect eosinophilic chronic rhinosinusitis.

Journal: International forum of allergy & rhinology
PMID: 39385680

Abstract

While typically diagnosed with biopsy, ECRS may be predicted preoperatively with the use of AI. Various AI models have been used, with pooled sensitivity of 0.857 and specificity of 0.850. We found no statistically significant difference between the accuracy of various AI models.

Authors

  • Jayanth Rajan
    Department of Surgery, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
  • Ross Rosen
    Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Daniel Karasik
    Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • John Richter
    Department of Otolaryngology-Head and Neck Surgery, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
  • Claudia Cabrera
    Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Brian D'Anza
    Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Kenneth Rodriguez
    Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Sanjeet V Rangarajan
    Department of Neurosurgery, University of Tennessee Health Science Center, Memphis, Tennessee, USA.

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