Clinical Evaluation of keyword-based, computer-generated reports of sinus operations

Wildfeuer, Valentina; Kunz, Viktor; Bieck, Richard; Sorge, Martin; Pirlich, M

doi:10.1055/s-0041-1727682

Cited by 1 publication

(6 citation statements)

References 0 publications

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“…Of the 79 articles identified, 3 were published in 2017, 17–19 5 were published in 2018, 20–24 9 were published in 2019, 25–33 15 were published in 2020, 34–48 31 were published in 2021, 49–79 and 16 were published in 2022 80…”

Section: Resultsmentioning

confidence: 99%

“…17,18,20,23,26–28,30,36,49,53,57,60,61,66,68,70,83 Of the cross-sectional studies, 7 were presented at scientific conferences as abstracts, 20,32,40,47,56,78,95 and 1 was presented at a conference as a poster. 77…”

Section: Resultsmentioning

confidence: 99%

“…Articles were published in journals of otolaryngology (n = 19), 17,18,23,26,28,30,36,38,39,42,46,52,57,65,72,77,86,87,94 radiology (n = 21), 19,22,25,29,31,33,34,45,48,51,54,55,58,67,73,74,81,83–85,95 medical sciences (n = 19), 32,41,44,47,53,61,63,66,68–70,76,78–80,89–91,93 or other areas of medicine (n = 20). 20,21,24,27,35,37,…”

Section: Resultsmentioning

confidence: 99%

“…Surgical planning and assessment The use of AI in surgical planning and assessment was explored by 8 studies. 34,35,56,71,73,74,77,90 Each study was unique in their objective, including prediction of nasal bone morphology based on photographs of the nose (n = 1), 90 automated analysis of surgical steps and identification of structures during pituitary surgery (n = 2), 56,71 real-time 3-dimentional anatomical annotation during transnasal surgery (n = 1), 73 automating the generation of operation reports in sinus surgery (n = 1), 77 prediction of navigational positions during endoscopy (n = 1), 34 automated prediction of whether patients had had rhinoplasty based on mobile photographs (n = 1), 35 and automated clipping of nasal endoscopic videos (n = 1). 74 ML was utilized in 7 of the studies 34,35,56,71,73,74,90 ; NLP was used in 2 of the studies.…”

Section: Resultsmentioning

confidence: 99%

“…17,18,20,23,[26][27][28]30,36,49,53,57,60,61,66,68,70,83 Of the crosssectional studies, 7 were presented at scientific conferences as abstracts, 20,32,40,47,56,78,95 and 1 was presented at a conference as a poster. 77 Articles were published in journals of otolaryngology (n = 19), 17,18,23,26,28,30,36,38,39,42,46,52,57,65,72,77,86,87,94 radiology (n = 21), 19,22,25,29,31,33,…”

Section: Characteristicsmentioning

confidence: 99%

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A Scoping Review of Artificial Intelligence Research in Rhinology

Kaul

Alvarado

Katsoulotos

et al. 2023

Am J Rhinol�Allergy

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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.

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