A Scoping Review of Artificial Intelligence Research in Rhinology. Issue 4 (July 2023)

• Record Type: Journal Article
• Authors: Osie, Gabriel; Darbari Kaul, Rhea; Alvarado, Raquel; Katsoulotos, Gregory; Rimmer, Janet; Kalish, Larry; Campbell, Raewyn G.; Sacks, Raymond; Harvey, Richard J.
• Is Part Of: American journal of rhinology & allergy. Volume 37:Issue 4(2023)
• Page Start: 438
• Page End: 448
• Publication Date: 2023-07
• Subjects: artificial intelligence -- artificial neural network -- convolutional neural network -- machine learning -- rhinology -- phenotyping -- endotyping -- diagnostics -- prognostics -- sinus; Nose -- Periodicals; Allergy -- Periodicals; 616.21005
• DOI: 10.1177/19458924231162437
• Languages: English
• ISSNs: 1945-8924

Abstract

Background: A considerable volume of possible applications of artificial intelligence (AI) in the field of rhinology exists, and research in the area is rapidly evolving. Objective: This scoping review aims to provide a brief overview of all current literature on AI in the field of rhinology. Further, it aims to highlight gaps in the literature for future rhinology researchers. Methods: OVID MEDLINE (1946-2022) and EMBASE (1974-2022) were searched from January 1, 2017 until May 14, 2022 to identify all relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews checklist was used to guide the review. Results: A total of 2420 results were identified of which 62 met the eligibility criteria. A further 17 articles were included through bibliography searching, for a total of 79 articles on AI in rhinology. Each year resulted in an increase in the number of publications, from 3 articles published in 2017 to 31 articles published in 2021. Articles were produced by authors from 22 countries with a relative majority coming from the USA (19%), China (19%), and South Korea (13%). Articles were placed into 1 of 5 categories: phenotyping/endotyping (n = 12), radiological diagnostics (n = 42), prognostication (n = 10), non-radiological diagnostics (n = 7), surgical assessment/planning (n = 8). Diagnostic or prognostic utility of the AI algorithms were rated as excellent (n = 29), very good (n = 25), good (n = 7), sufficient (n = 1), bad (n = 2), or was not reported/not applicable (n = 15). Conclusions: AI is experiencing an increasingly significant role in rhinology research. Articles are showing high rates of diagnostic accuracy and are being published at an almost exponential rate around the world. Utilizing AI in radiological diagnosis was the most published topic of research, however, AI in rhinology is still in its infancy and there are several topics yet to be thoroughly explored.