The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.

Journal: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PMID: 39230611

Abstract

PURPOSE: To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.

Authors

  • Pakapoom Sukswai
    Department of Otolaryngology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
  • Narit Hnoohom
    Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.
  • Minh Phuoc Hoang
    Department of Otolaryngology, Hue University of Medicine and Pharmacy, Hue University, Hue, Vietnam.
  • Songklot Aeumjaturapat
    Endoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
  • Supinda Chusakul
    Endoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
  • Jesada Kanjanaumporn
    Endoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
  • Kachorn Seresirikachorn
    Endoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
  • Kornkiat Snidvongs
    Endoscopic Nasal and Sinus Surgery Excellence Center, Department of Otolaryngology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand. drkornkiat@yahoo.com.

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