| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Silva, Helbert Eustáquio Cardoso da | - |
| dc.contributor.author | Santos, Glaucia Nize Martins | - |
| dc.contributor.author | Leite, André Ferreira | - |
| dc.contributor.author | Mesquita, Carla Ruffeil Moreira | - |
| dc.contributor.author | Figueiredo, Paulo Tadeu de Souza | - |
| dc.contributor.author | Stefani, Cristine Miron | - |
| dc.contributor.author | Melo, Nilce Santos de | - |
| dc.date.accessioned | 2024-01-22T12:17:01Z | - |
| dc.date.available | 2024-01-22T12:17:01Z | - |
| dc.date.issued | 2023-10-05 | - |
| dc.identifier.citation | SILVA, Helbert Eustáquio Cardoso da et al. The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods : an overview of the systematic reviews. PLoS ONE, v. 18, n. 10, e0292063, 2023. DOI: https://doi.org/10.1371/journal.pone.0292063. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292063. Acesso em: 22 jan. 2024. | pt_BR |
| dc.identifier.uri | http://repositorio2.unb.br/jspui/handle/10482/47428 | - |
| dc.language.iso | eng | pt_BR |
| dc.publisher | Plos One | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods : an overview of the systematic reviews | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Inteligência artificial | pt_BR |
| dc.subject.keyword | Diagnóstico por imagem | pt_BR |
| dc.subject.keyword | Câncer - diagnóstico | pt_BR |
| dc.rights.license | Copyright: © 2023 Silva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | pt_BR |
| dc.identifier.doi | https://doi.org/10.1371/journal.pone.0292063 | pt_BR |
| dc.description.abstract1 | Background and purpose
In comparison to conventional medical imaging diagnostic modalities, the aim of this over view article is to analyze the accuracy of the application of Artificial Intelligence (AI) tech niques in the identification and diagnosis of malignant tumors in adult patients.
Data sources
The acronym PIRDs was used and a comprehensive literature search was conducted on
PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and
grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI
as a diagnostic model and/or detection tool for any cancer type in adult patients, compared
to the traditional diagnostic radiographic imaging model. There were no limits on publishing
status, publication time, or language. For study selection and risk of bias evaluation, pairs of
reviewers worked separately.
Results
In total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 sat isfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis.
Although there was heterogeneity in terms of methodological aspects, patient differences,
and techniques used, the studies found that several AI approaches are promising in terms
of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malig nant tumors. When compared to other machine learning algorithms, the Super Vector
Machine method performed better in cancer detection and diagnosis. Computer-assisted
detection (CAD) has shown promising in terms of aiding cancer detection, when compared
to the traditional method of diagnosis.Conclusions
The detection and diagnosis of malignant tumors with the help of AI seems to be feasible
and accurate with the use of different technologies, such as CAD systems, deep and
machine learning algorithms and radiomic analysis when compared with the traditional
model, although these technologies are not capable of to replace the professional radiologist
in the analysis of medical images. Although there are limitations regarding the generalization
for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and
teaching tool, especially for less trained professionals. Therefore, further longitudinal stud ies with a longer follow-up duration are required for a better understanding of the clinical
application of these artificial intelligence systems. | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0003-2662-6987 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0003-4712-9779 | pt_BR |
| dc.contributor.affiliation | Brasilia University, Faculty of Health Science, Dentistry of Department | pt_BR |
| dc.contributor.affiliation | Brasilia University, Faculty of Health Science, Dentistry of Department | pt_BR |
| dc.contributor.affiliation | Brasilia University, Faculty of Health Science, Dentistry of Department | pt_BR |
| dc.contributor.affiliation | Brasilia University, Faculty of Health Science, Dentistry of Department | pt_BR |
| dc.contributor.affiliation | Brasilia University, Faculty of Health Science, Dentistry of Department | pt_BR |
| dc.contributor.affiliation | Brasilia University, Faculty of Health Science, Dentistry of Department | pt_BR |
| dc.contributor.affiliation | Brasilia University, Faculty of Health Science, Dentistry of Department | pt_BR |
| dc.description.unidade | Faculdade de Ciências da Saúde (FS) | pt_BR |
| dc.description.unidade | Departamento de Odontologia (FS ODT) | pt_BR |
| dc.description.ppg | Programa de Pós-Graduação em Odontologia | pt_BR |
| Aparece nas coleções: | Artigos publicados em periódicos e afins
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