Artificial Intelligence in Laryngology, Broncho-Esophagology, and Sleep Surgery

Obinna I Nwosu; Matthew R Naunheim

doi:10.1016/j.otc.2024.04.002

Artificial Intelligence in Laryngology, Broncho-Esophagology, and Sleep Surgery

Otolaryngol Clin North Am. 2024 Oct;57(5):821-829. doi: 10.1016/j.otc.2024.04.002. Epub 2024 May 7.

Authors

Obinna I Nwosu¹, Matthew R Naunheim²

Affiliations

¹ Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, MA, USA; Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, MA, USA.
² Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, MA, USA; Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, MA, USA. Electronic address: mnaunheim@mgb.org.

PMID: 38719714
DOI: 10.1016/j.otc.2024.04.002

Abstract

Technological advancements in laryngology, broncho-esophagology, and sleep surgery have enabled the collection of increasing amounts of complex data for diagnosis and treatment of voice, swallowing, and sleep disorders. Clinicians face challenges in efficiently synthesizing these data for personalized patient care. Artificial intelligence (AI), specifically machine learning and deep learning, offers innovative solutions for processing and interpreting these data, revolutionizing diagnosis and management in these fields, and making care more efficient and effective. In this study, we review recent AI-based innovations in the fields of laryngology, broncho-esophagology, and sleep surgery.

Keywords: Artificial intelligence; Broncho-esophagology; Deep learning; Laryngology; Machine learning; Otolaryngology; Sleep.

Publication types

Review

MeSH terms

Artificial Intelligence*
Deep Learning
Humans
Machine Learning
Otolaryngology / methods
Sleep Wake Disorders / diagnosis