Multi-institutional validation of an AI-based sinus CT analytic platform with olfactory assessments.

Massey, Conner J; Humphries, Stephen M; Mace, Jess C; Smith, Timothy L; Soler, Zachary M; Ramakrishnan, Vijay R

Massey, Conner J; Humphries, Stephen M; Mace, Jess C; Smith, Timothy L; Soler, Zachary M; Ramakrishnan, Vijay R.

Afiliação

Massey CJ; Department of Otolaryngology-Head & Neck Surgery, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Humphries SM; Department of Radiology, National Jewish Health, Denver, Colorado, USA.
Mace JC; Department of Otolaryngology-Head & Neck Surgery, Oregon Health Sciences University, Portland, Oregon, USA.
Smith TL; Department of Otolaryngology-Head & Neck Surgery, Oregon Health Sciences University, Portland, Oregon, USA.
Soler ZM; Department of Otolaryngology-Head & Neck Surgery, Medical University of South Carolina, Charleston, South Carolina, USA.
Ramakrishnan VR; Department of Otolaryngology-Head & Neck Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Int Forum Allergy Rhinol ; 2024 Jul 12.

Article em En | MEDLINE | ID: mdl-38995344

ABSTRACT

ABSTRACT

KEY POINTS AI-based CT sinus analysis may have advantages over visual based systems, for example, Lund-Mackay score. Here, we show multi-institutional validation of an AI algorithm using novel OMC classification. Significant, robust correlations are seen between algorithm outputs and clinical outcomes.

Palavras-chave

artificial intelligence; chronic rhinosinusitis; computed tomography; olfaction; paranasal sinus

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Int Forum Allergy Rhinol Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

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