Fundação Edson Queiroz

Aims: To evaluate the use of low-level laser therapy in endodontic treatment and its broader applicability in General Health and Dentistry. Study Design: Literature review. Place and Duration of Study: The study involved searching articles and book chapters in the Google Scholar and PubMed databases from 2018 to 2023, with the review concluding in 2024. Methodology: The review used descriptors from the Brazilian Health Sciences Descriptors database: Laser Therapy, Low-Level Light Therapy, Endodontic Treatment, Dentistry, and their Portuguese equivalents. A total of 34 articles were selected, focusing exclusively on low-level laser therapy and its applications. Articles not addressing this specific therapy were excluded. Results: Low-level laser therapy is frequently used as an adjunct in general health, dentistry and endodontic treatment. Clinical applications, such as postoperative pain control, root canal system disinfection, periapical surgery, and tissue repair, showed promising results. However, more theoretical and clinical evidence is still required to fully establish its efficacy. Further research is needed to strengthen the evidence base and optimize its clinical use. Conclusion: Low-level laser therapy offers a potential non-invasive treatment option in endodontics, with satisfactory outcomes in various clinical applications.

Artificial intelligence (AI) algorithms for the detection of retinoblastoma (RB) by fundus image analysis have been proposed as a potentially effective technique to facilitate diagnosis and screening programs. However, doubts remain about the accuracy of the technique, the best type of AI for this situation, and its feasibility for everyday use. Therefore, we performed a systematic review and meta-analysis to evaluate this issue. Following PRISMA 2020 guidelines, a comprehensive search of MEDLINE, Embase,ClinicalTrials.gov and IEEEX databases identified494studies whose titles and abstracts were screened for eligibility. We included diagnostic studies that evaluated the accuracy of AI in identifying retinoblastoma based on fundus imaging. Univariate and bivariate analysis was performed using the random effects model. The study protocol was registered in PROSPERO under CRD42024499221. Six studies with 9902 fundus images were included, of which 5944 (60%) had confirmed RB. Only one dataset used a semi-supervised machine learning (ML) based method, all other studies used supervised ML, three using architectures requiring high computational power and two using more economical models. The pooled analysis of all models showed a sensitivity of 98.2% (95% CI: 0.947-0.994), a specificity of 98.5% (95% CI: 0.916-0.998) and an AUC of 0.986 (95% CI: 0.970-0.989). Subgroup analyses comparing models with high and low computational power showed no significant difference (p=0.824). AI methods showed a high precision in the diagnosis of RB based on fundus images with no significant difference when comparing high and low computational power models, suggesting a viability of their use. Validation and cost-effectiveness studies are needed in different income countries. Subpopulations should also be analyzed, as AI may be useful as an initial screening tool in populations at high risk for RB, serving as a bridge to the pediatric ophthalmologist or ocular oncologist, who are scarce globally. What isknown Retinoblastoma is the most common intraocular cancer in childhood and diagnostic delay is the main factor leading to a poor prognosis. The application of machine learning techniques proposes reliable methods for screening and diagnosis of retinal diseases. What isnew The meta-analysis of the diagnostic accuracy of artificial intelligence methods for diagnosing retinoblastoma based on fundus images showed a sensitivity of 98.2% (95% CI: 0.947-0.994) and a specificity of 98.5% (95% CI: 0.916-0.998). There was no statistically significant difference in the diagnostic accuracy of high and low computational power models. The overall performance of supervised machine learning was best than unsupervised, although few studies were available on the second type.

Os levantamentos sobre os fatores associados sade mental durante uma pandemia podem subsidiar estratgias de interveno eficazes. Respondendo a essa demanda, objetivou-se investigar as variveis associadas sade mental (sade geral, ansiedade, depresso e estresse percebido) de brasileiros durante a pandemia da coronavirus disease 2019 . Realizou-se um estudo de levantamento nacional que contou com uma amostra de 2.705 brasileiros, que responderam a seis instrumentos que abordavam dados sociodemogrficos e clnicos relacionados Covid-19, a adeso s orientaes de controle da pandemia, o consumo de informao, o enfrentamento, a sade geral e o estresse percebido. Os dados foram analisados por meio de estatstica descritiva e analtica. Os resultados apontaram que a concordncia com o distanciamento social e a adeso a ele, a experincia de adoecimento, ser pessoa do grupo de risco ou morar com indivduos com essa caracterstica, o menor consumo de informao e o menor enfrentamento esto associados ao adoecimento em sade mental. Concluiu-se que necessria a interveno contnua em sade mental durante a pandemia.