Gabriel Osie, Rhea Darbari Kaul, Raquel Alvarado, Gregory Katsoulotos, Janet Rimmer, Larry Kalish, Raewyn G Campbell, Raymond Sacks, Richard J Harvey
{"title":"A Scoping Review of Artificial Intelligence Research in Rhinology.","authors":"Gabriel Osie, Rhea Darbari Kaul, Raquel Alvarado, Gregory Katsoulotos, Janet Rimmer, Larry Kalish, Raewyn G Campbell, Raymond Sacks, Richard J Harvey","doi":"10.1177/19458924231162437","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>A considerable volume of possible applications of artificial intelligence (AI) in the field of rhinology exists, and research in the area is rapidly evolving.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":7650,"journal":{"name":"American Journal of Rhinology & Allergy","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10273866/pdf/","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Rhinology & Allergy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/19458924231162437","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OTORHINOLARYNGOLOGY","Score":null,"Total":0}
引用次数: 1
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.
期刊介绍:
The American Journal of Rhinology & Allergy is a peer-reviewed, scientific publication committed to expanding knowledge and publishing the best clinical and basic research within the fields of Rhinology & Allergy. Its focus is to publish information which contributes to improved quality of care for patients with nasal and sinus disorders. Its primary readership consists of otolaryngologists, allergists, and plastic surgeons. Published material includes peer-reviewed original research, clinical trials, and review articles.