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Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review
Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative tr...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672270/ https://www.ncbi.nlm.nih.gov/pubmed/38002588 http://dx.doi.org/10.3390/jcm12226973 |
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author | Tsilivigkos, Christos Athanasopoulos, Michail Micco, Riccardo di Giotakis, Aris Mastronikolis, Nicholas S. Mulita, Francesk Verras, Georgios-Ioannis Maroulis, Ioannis Giotakis, Evangelos |
author_facet | Tsilivigkos, Christos Athanasopoulos, Michail Micco, Riccardo di Giotakis, Aris Mastronikolis, Nicholas S. Mulita, Francesk Verras, Georgios-Ioannis Maroulis, Ioannis Giotakis, Evangelos |
author_sort | Tsilivigkos, Christos |
collection | PubMed |
description | Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords ‘otorhinolaryngology’, ‘imaging’, ‘computer vision’, ‘artificial intelligence’, and ‘deep learning’, and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology. |
format | Online Article Text |
id | pubmed-10672270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106722702023-11-08 Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review Tsilivigkos, Christos Athanasopoulos, Michail Micco, Riccardo di Giotakis, Aris Mastronikolis, Nicholas S. Mulita, Francesk Verras, Georgios-Ioannis Maroulis, Ioannis Giotakis, Evangelos J Clin Med Review Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords ‘otorhinolaryngology’, ‘imaging’, ‘computer vision’, ‘artificial intelligence’, and ‘deep learning’, and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology. MDPI 2023-11-08 /pmc/articles/PMC10672270/ /pubmed/38002588 http://dx.doi.org/10.3390/jcm12226973 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Tsilivigkos, Christos Athanasopoulos, Michail Micco, Riccardo di Giotakis, Aris Mastronikolis, Nicholas S. Mulita, Francesk Verras, Georgios-Ioannis Maroulis, Ioannis Giotakis, Evangelos Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review |
title | Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review |
title_full | Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review |
title_fullStr | Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review |
title_full_unstemmed | Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review |
title_short | Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review |
title_sort | deep learning techniques and imaging in otorhinolaryngology—a state-of-the-art review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672270/ https://www.ncbi.nlm.nih.gov/pubmed/38002588 http://dx.doi.org/10.3390/jcm12226973 |
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