Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review.

Publication/Presentation Date

10-1-2022

Abstract

OBJECTIVES/HYPOTHESIS: This scoping review aims to provide a broad overview of the applications of artificial intelligence (AI) to office laryngoscopy to identify gaps in knowledge and guide future research.

STUDY DESIGN: Scoping Review.

METHODS: Searches for studies on AI and office laryngoscopy were conducted in five databases. Title and abstract and then full-text screening were performed. Primary research studies published in English of any date were included. Studies were summarized by: AI applications, targeted conditions, imaging modalities, author affiliations, and dataset characteristics.

RESULTS: Studies focused on vocal fold vibration analysis (43%), lesion recognition (24%), and vocal fold movement determination (19%). The most frequently automated tasks were recognition of vocal fold nodules (19%), polyp (14%), paralysis (11%), paresis (8%), and cyst (7%). Imaging modalities included high-speed laryngeal videos (45%), stroboscopy (29%), and narrow band imaging endoscopy (7%). The body of literature was primarily authored by science, technology, engineering, and math (STEM) specialists (76%) with only 30 studies (31%) involving co-authorship by STEM specialists and otolaryngologists. Datasets were mostly from single institution (84%) and most commonly originated from Germany (23%), USA (16%), Spain (9%), Italy (8%), and China (8%). Demographic information was only reported in 39 studies (40%), with age and sex being the most commonly reported, whereas race/ethnicity and gender were not reported in any studies.

CONCLUSION: More interdisciplinary collaboration between STEM and otolaryngology research teams improved demographic reporting especially of race and ethnicity to ensure broad representation, and larger and more geographically diverse datasets will be crucial to future research on AI in office laryngoscopy.

LEVEL OF EVIDENCE: NA Laryngoscope, 132:1993-2016, 2022.

Volume

132

Issue

10

First Page

1993

Last Page

2016

ISSN

1531-4995

Disciplines

Medicine and Health Sciences

PubMedID

34582043

Department(s)

Department of Surgery Residents, Fellows and Residents

Document Type

Article

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