Antonio Mario Bulfamante1, Francesco Ferella1, Austin Michael Miller2, Cecilia Rosso1, Carlotta Pipolo1, Emanuela Fuccillo1,3, Giovanni Felisati1, Alberto Maria Saibene4. 1. Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università degli Studi di Milano, Milan, Italy. 2. Ohio University Heritage College of Osteopathic Medicine, Dublin, OH, USA. 3. Department of Clinical Sciences and Translational Medicine, Università degli Studi di Roma Tor Vergata, Rome, Italy. 4. Otolaryngology Unit, Santi Paolo e Carlo Hospital, Department of Health Sciences, Università degli Studi di Milano, Milan, Italy. alberto.saibene@unimi.it.
Abstract
PURPOSE: This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability. METHODS: MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov databases. Search criteria were designed to include all studies published until December 2021 presenting or employing AI for rhinological applications. We selected all original studies specifying AI models reliability. After duplicate removal, abstract and full-text selection, and quality assessment, we reviewed eligible articles for data pool size, AI tools used, input and outputs, and model reliability. RESULTS: Among 1378 unique citations, 39 studies were deemed eligible. Most studies (n = 29) were technical papers. Input included compiled data, verbal data, and 2D images, while outputs were in most cases dichotomous or selected among nominal classes. The most frequently employed AI tools were support vector machine for compiled data and convolutional neural network for 2D images. Model reliability was variable, but in most cases was reported to be between 80% and 100%. CONCLUSIONS: AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process.
PURPOSE: This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability. METHODS: MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov databases. Search criteria were designed to include all studies published until December 2021 presenting or employing AI for rhinological applications. We selected all original studies specifying AI models reliability. After duplicate removal, abstract and full-text selection, and quality assessment, we reviewed eligible articles for data pool size, AI tools used, input and outputs, and model reliability. RESULTS: Among 1378 unique citations, 39 studies were deemed eligible. Most studies (n = 29) were technical papers. Input included compiled data, verbal data, and 2D images, while outputs were in most cases dichotomous or selected among nominal classes. The most frequently employed AI tools were support vector machine for compiled data and convolutional neural network for 2D images. Model reliability was variable, but in most cases was reported to be between 80% and 100%. CONCLUSIONS: AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process.
Authors: Giovanni Dimauro; Giorgio Ciprandi; Francesca Deperte; Francesco Girardi; Enrico Ladisa; Sergio Latrofa; Matteo Gelardi Journal: Int J Med Inform Date: 2018-11-30 Impact factor: 4.046
Authors: Naweed I Chowdhury; Timothy L Smith; Rakesh K Chandra; Justin H Turner Journal: Int Forum Allergy Rhinol Date: 2018-08-11 Impact factor: 3.858
Authors: S Hassid; C Decaestecker; C Hermans; I Salmon; J L Pasteels; A Danguy; R Kiss Journal: Ann Otol Rhinol Laryngol Date: 1997-12 Impact factor: 1.547
Authors: Alessandro Liberati; Douglas G Altman; Jennifer Tetzlaff; Cynthia Mulrow; Peter C Gøtzsche; John P A Ioannidis; Mike Clarke; P J Devereaux; Jos Kleijnen; David Moher Journal: BMJ Date: 2009-07-21