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...
Autores principales: | , , , , |
---|---|
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 The average accuracy of detection is high (96.84%), but the usage of private and outdated datasets with a majority of clinical data is very common.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 |