Cargando…

A systematic review on artificial intelligence techniques for detecting thyroid diseases

The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discove...

Descripción completa

Detalles Bibliográficos
Autores principales: Aversano, Lerina, Bernardi, Mario Luca, Cimitile, Marta, Maiellaro, Andrea, Pecori, Riccardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280452/
https://www.ncbi.nlm.nih.gov/pubmed/37346658
http://dx.doi.org/10.7717/peerj-cs.1394
_version_ 1785060797538369536
author Aversano, Lerina
Bernardi, Mario Luca
Cimitile, Marta
Maiellaro, Andrea
Pecori, Riccardo
author_facet Aversano, Lerina
Bernardi, Mario Luca
Cimitile, Marta
Maiellaro, Andrea
Pecori, Riccardo
author_sort Aversano, Lerina
collection PubMed
description The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection.
format Online
Article
Text
id pubmed-10280452
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-102804522023-06-21 A systematic review on artificial intelligence techniques for detecting thyroid diseases Aversano, Lerina Bernardi, Mario Luca Cimitile, Marta Maiellaro, Andrea Pecori, Riccardo PeerJ Comput Sci Bioinformatics The use of artificial intelligence approaches in health-care systems has grown rapidly over the last few years. In this context, early detection of diseases is the most common area of application. In this scenario, thyroid diseases are an example of illnesses that can be effectively faced if discovered quite early. Detecting thyroid diseases is crucial in order to treat patients effectively and promptly, by saving lives and reducing healthcare costs. This work aims at systematically reviewing and analyzing the literature on various artificial intelligence-related techniques applied to the detection and identification of various diseases related to the thyroid gland. The contributions we reviewed are classified according to different viewpoints and taxonomies in order to highlight pros and cons of the most recent research in the field. After a careful selection process, we selected and reviewed 72 papers, analyzing them according to three main research questions, i.e., which diseases of the thyroid gland are detected by different artificial intelligence techniques, which datasets are used to perform the aforementioned detection, and what types of data are used to perform the detection. The review demonstrates that the majority of the considered papers deal with supervised methods to detect hypo- and hyperthyroidism. The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common. Finally, we discuss the outcomes of the systematic review, pointing out advantages, disadvantages, and future developments in the application of artificial intelligence for thyroid diseases detection. PeerJ Inc. 2023-06-06 /pmc/articles/PMC10280452/ /pubmed/37346658 http://dx.doi.org/10.7717/peerj-cs.1394 Text en ©2023 Aversano et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Aversano, Lerina
Bernardi, Mario Luca
Cimitile, Marta
Maiellaro, Andrea
Pecori, Riccardo
A systematic review on artificial intelligence techniques for detecting thyroid diseases
title A systematic review on artificial intelligence techniques for detecting thyroid diseases
title_full A systematic review on artificial intelligence techniques for detecting thyroid diseases
title_fullStr A systematic review on artificial intelligence techniques for detecting thyroid diseases
title_full_unstemmed A systematic review on artificial intelligence techniques for detecting thyroid diseases
title_short A systematic review on artificial intelligence techniques for detecting thyroid diseases
title_sort systematic review on artificial intelligence techniques for detecting thyroid diseases
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280452/
https://www.ncbi.nlm.nih.gov/pubmed/37346658
http://dx.doi.org/10.7717/peerj-cs.1394
work_keys_str_mv AT aversanolerina asystematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT bernardimarioluca asystematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT cimitilemarta asystematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT maiellaroandrea asystematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT pecoririccardo asystematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT aversanolerina systematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT bernardimarioluca systematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT cimitilemarta systematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT maiellaroandrea systematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases
AT pecoririccardo systematicreviewonartificialintelligencetechniquesfordetectingthyroiddiseases