Artificial intelligence based diagnosis of sulcus: assesment of videostroboscopy via deep learning.

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
Published Date: Jul 13, 2024

Abstract

PURPOSE: To develop a convolutional neural network (CNN)-based model for classifying videostroboscopic images of patients with sulcus, benign vocal fold (VF) lesions, and healthy VFs to improve clinicians' accuracy in diagnosis during videostroboscopies when evaluating sulcus.

Authors

  • Ömer Tarık Kavak
    Department of Otorhinolaryngology, Marmara University Faculty of Medicine, Pendik Training and Research Hospital, Fevzi Çakmak Muhsin Yazıcıoğlu Street, İstanbul, 34899, Turkey. omrkavak11@gmail.com.
  • Şevket Gündüz
    VRLab Academy, 32 Willoughby Rd, Harringay Ladder, London, N8 0JG, UK.
  • Cabir Vural
    Marmara University Faculty of Engineering, Electrical and Electronics Engineering, Başıbüyük, RTE Campus, İstanbul, 34854, Turkey.
  • Necati Enver
    Department of Otolaryngology-Head and Neck Surgery, Marmara University Faculty of Medicine, Istanbul, Turkey.

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