Avaliação do ensino a distância no curso de graduação em odontologia

Os currículos dos cursos de Odontologia estão em processo de modernização, para se adequarem ao mercado de trabalho voltado às necessidades da população. As mudanças buscam um método de ensino que propicie ao aluno a autogestão do seu conhecimento através de uma visão transdisciplinar. Assim, o objetivo desse trabalho é fazer uma reflexão acerca da formação dos docentes responsáveis pelas disciplinas de Odontologia, bem como das novas possibilidades de transmissão do conhecimento. Nesse contexto, a educação a distância se mostra como alternativa para a educação continuada dos futuros dentistas, abrangendo novas tecnologias e novos métodos de ensino de adultos. Por meio de uma revisão da literatura, discute-se sobre as metas das diretrizes curriculares nacionais e sobre a formação de professores dos cursos de Odontologia e, ainda, são apresentadas pesquisas em educação a distância na área de saúde. Os resultados desse estudo revelam sucesso na aplicação do ensino a distância para alunos adultos nos cursos de Odontologia

Avaliação quanto aos indicadores de qualidade em odontologia na Secretaria de Saúde do Distrito Federal

Objetivo: O presente estudo procurou avaliar a gestão da qualidade no serviço público odontológico do Distrito Federal frente aos conceitos de qualidade de vida e satisfação do usuário, verificando o uso de indicadores de qualidade e instrumentos de gestão pelos coordenadores de odontologia na Secretaria de Estado da Saúde do DF. Método: A avaliação será realizada através de questionário, que contem 11 (onze) perguntas, tem o intuito de verificar o conhecimento dos gestores sobre a gestão da qualidade, utilização de instrumentos de gestão, modelo aplicado atualmente, efeitos sobre o atendimento no serviço público e satisfação do usuário. Resultados: Ficou demonstrado neste estudo que existe uma necessidade evidente de se estimular a discussão dos profissionais da odontologia quanto a gestão da qualidade no serviço público, estabelecendo critérios de contextualização social, pois sabe-se da enorme desigualdade social que requer formas diferentes de organização dos serviços com a finalidade de se oferecer serviços equânimes e de qualidade vistos na visão do usuário, seguindo os princípios e diretrizes organizacionais do SUS

Doença periodontal em crianças e adolescentes com diagnóstico de Diabetes Mellitus: Periodontal disease in children and adolescents diagnosed with Diabetes Mellitus

Os pacientes portadores do Diabetes Mellitus (DM), tipo 1 e tipo 2, possuem maiores chances de desenvolver doenças periodontais com maior velocidade de progressão e gravidade. O objetivo do estudo é apresentar a relação bidirecional entre por meio da revisão da literatura, a relação bidirecional entre Doença Periodontal (DP) e Diabetes Mellitus em crianças e adolescentes. Foram pesquisados artigos de estudos observacionais nas bases de dados BVS (Lilacs e Bbo), Pubmed e Scielo, publicados entre 2008 e 2018. Os artigos evidenciaram que a Doença Periodontal tem característica inflamatória multifatorial, que em estágios mais graves pode evoluir para mobilidade e perda precoce dos dentes. Desta forma, a literatura evidencia uma forte ligação bidirecional entre a saúde bucal e a saúde sistêmica, sendo a Diabetes Mellitus um fator de risco para a periodontite em crianças e adolescentes. Esse estudo reitera a relação bilateral entre doença periodontal e Diabetes Mellitus e demonstra que crianças e adolescentes com essa condição sistêmica podem sofrer alterações na resistência bacteriana e exacerbar infecções existentes

EVALUATION OF DISTANCE EDUCATION IN DENTISTRY GRADUATION COURSE

The curricula of Dentistry are in the process of modernization , to suit the labor market geared to the needs of the population . The changes seek a teaching method that is conducive to student elfmanagement of their knowledge through a transdisciplinary vision. Objective: To examine about the training of teachers responsible for the disciplines of dentistry , as well as new possibilities of knowledge transmission . In this context, the distance is shown as an alternative to the continuing education of future dentists , covering new technologies and new methods of teaching adults. Methods: Through a literature review , we discuss about the goals of the national curriculum guidelines and the training of teachers of courses in dentistry and also surveys are presented in distance education in healthcare. Results: The study shows successful application of distance learning for adult learners courses in Dentistry

The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews.

Background and purposeIn comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients.Data sourcesThe 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.ResultsIn total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied 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 malignant 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.ConclusionsThe 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 studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems.Trial registrationSystematic review registration. Prospero registration number: CRD42022307403

The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods : an overview of the systematic reviews

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.Faculdade de Ciências da Saúde (FS)Departamento de Odontologia (FS ODT)Programa de Pós-Graduação em Odontologi

Flow diagram of the literature search and selection criteria.

Flow diagram of the literature search and selection criteria.</p

PRISMA 2009 checklist.

Background and purposeIn comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients.Data sourcesThe 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.ResultsIn total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied 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 malignant 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.ConclusionsThe 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 studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems.Trial registrationSystematic review registration. Prospero registration number: CRD42022307403.</div

Overlaping (n = 09).

Background and purposeIn comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients.Data sourcesThe 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.ResultsIn total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied 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 malignant 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.ConclusionsThe 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 studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems.Trial registrationSystematic review registration. Prospero registration number: CRD42022307403.</div