Enhancing nasal endoscopy: Classification, detection, and segmentation of anatomic landmarks using a convolutional neural network.

Ganeshan, Vinayak; Bidwell, Jonathan; Gyawali, Dipesh; Nguyen, Thinh S; Morse, Jonathan; Smith, Madeline P; Barton, Blair M; McCoul, Edward D

Ganeshan, Vinayak; Bidwell, Jonathan; Gyawali, Dipesh; Nguyen, Thinh S; Morse, Jonathan; Smith, Madeline P; Barton, Blair M; McCoul, Edward D.

Afiliação

Ganeshan V; Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
Bidwell J; Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
Gyawali D; Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
Nguyen TS; Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
Morse J; Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.
Smith MP; Ochsner Clinical School, University of Queensland, New Orleans, Louisiana, USA.
Barton BM; Department of Otolaryngology, Tulane University School of Medicine, New Orleans, Louisiana, USA.
McCoul ED; Department of Otorhinolaryngology, Ochsner Health, New Orleans, Louisiana, USA.

Int Forum Allergy Rhinol ; 2024 Jun 10.

Article em En | MEDLINE | ID: mdl-38853655

ABSTRACT

ABSTRACT

KEY POINTS A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.

Palavras-chave

artificial intelligence; deep learning; machine learning; nasal endoscopy; neural network; turbinate

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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

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