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Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review.

Antonio Mario Bulfamante1, Francesco Ferella1, Austin Michael Miller2

  • 1Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università degli Studi di Milano, Milan, Italy.

European Archives of Oto-Rhino-Laryngology : Official Journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
|October 19, 2022
PubMed
Summary

Artificial intelligence (AI) shows great promise in rhinology, but limited code sharing hinders research progress. Current AI tools require extensive data preprocessing, impacting the use of raw clinical data.

Keywords:
AlgorithmAllergyAnatomyParanasal sinusesRadiomicsRhinitis

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Area of Science:

  • This systematic review explores the burgeoning field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) applications within rhinology.
  • Focuses on PRISMA guidelines for systematic review methodology.

Background:

  • Analyzes existing AI applications in rhinology, comparing studies on data size, AI systems, input/output parameters, and model reliability.
  • Highlights the potential of AI to advance rhinological research and clinical practice.

Approach:

  • Conducted a systematic review of MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov up to December 2021.
  • Included original studies detailing AI model reliability, with rigorous selection and quality assessment processes.

Key Points:

  • 39 studies were eligible from 1378 unique citations; 29 were technical papers.
  • Common inputs included compiled data, verbal data, and 2D images; outputs were typically dichotomous or nominal.
  • Support vector machines and convolutional neural networks were frequently used AI tools; model reliability ranged from 80% to 100%.

Conclusions:

  • AI holds significant potential for rhinology, yet restricted code accessibility impedes research advancement.
  • Lack of shared code necessitates model reconstruction, slowing progress.
  • Current AI tools face limitations with raw clinical data, requiring substantial pre-analytical interpretation.