METHODS: Frontal view intraoral photographs fulfilling selection criteria were collected. Along the gingival margin, the gingival conditions of individual sites were labelled as healthy, diseased, or questionable. Photographs were randomly assigned as training or validation datasets. Training datasets were input into a novel artificial intelligence system and its accuracy in detection of gingivitis including sensitivity, specificity, and mean intersection-over-union were analysed using validation dataset. The accuracy was reported according to STARD-2015 statement.
RESULTS: A total of 567 intraoral photographs were collected and labelled, of which 80% were used for training and 20% for validation. Regarding training datasets, there were total 113,745,208 pixels with 9,270,413; 5,711,027; and 4,596,612 pixels were labelled as healthy, diseased, and questionable respectively. Regarding validation datasets, there were 28,319,607 pixels with 1,732,031; 1,866,104; and 1,116,493 pixels were labelled as healthy, diseased, and questionable, respectively. AI correctly predicted 1,114,623 healthy and 1,183,718 diseased pixels with sensitivity of 0.92 and specificity of 0.94. The mean intersection-over-union of the system was 0.60 and above the commonly accepted threshold of 0.50.
CONCLUSIONS: Artificial intelligence could identify specific sites with and without gingival inflammation, with high sensitivity and high specificity that are on par with visual examination by human dentist. This system may be used for monitoring of the effectiveness of patients' plaque control.
METHODS: Following PRISMA guidelines, we conducted a systematic literature search in PubMed and Google Scholar databases to identify studies reporting Strongyloides prevalence data in the 11 Southeast Asian countries up to December 2022. A random effects model was employed to estimate the pooled prevalence of S. stercoralis at both regional and country levels.
RESULTS: Out of 3722 articles identified, 224 met our inclusion criteria. For S. stercoralis specifically, we found 187 articles, of which 52.4% were from Thailand. All Southeast Asian countries, except Brunei, had at least one study on Strongyloides prevalence. The estimated pooled prevalence of S. stercoralis regionally was 12.7% (95% CI 10.70-14.80%), ranging from 0.4 to 24.9% at the country level. Cambodia had the highest pooled prevalence (24.9%, 95% CI 15.65-35.38%), followed by Lao PDR (16.5%, 95% CI 9.50-24.95%). Moreover, we obtained a pooled prevalence of 10% (95% CI 7.06-13.52%) in a group comprising immigrants, workers, and veterans from Southeast Asian countries. S. stercoralis infects various host types, including nonhuman primates, domestic dogs and cats, rodents, and transport carriers such as cockroaches and vegetables.
CONCLUSIONS: A high prevalence of strongyloidiasis in Southeast Asia was revealed, highlighting the importance of the region's ongoing research, surveillance, and control efforts. Factors contributing to the strongyloidiasis transmission include the role of animal hosts, the impact of global connectivity, and the significance of the co-endemicity of other Strongyloides species. Based on these findings, a multi-pronged One-Health approach is essential for sustainable intervention and control.
OBJECTIVE: This study aims to examine the potential for, and concerns of, using AI in scientific research. For this purpose, high-impact research articles were generated by analyzing the quality of reports generated by ChatGPT and assessing the application's impact on the research framework, data analysis, and the literature review. The study also explored concerns around ownership and the integrity of research when using AI-generated text.
METHODS: A total of 4 articles were generated using ChatGPT, and thereafter evaluated by 23 reviewers. The researchers developed an evaluation form to assess the quality of the articles generated. Additionally, 50 abstracts were generated using ChatGPT and their quality was evaluated. The data were subjected to ANOVA and thematic analysis to analyze the qualitative data provided by the reviewers.
RESULTS: When using detailed prompts and providing the context of the study, ChatGPT would generate high-quality research that could be published in high-impact journals. However, ChatGPT had a minor impact on developing the research framework and data analysis. The primary area needing improvement was the development of the literature review. Moreover, reviewers expressed concerns around ownership and the integrity of the research when using AI-generated text. Nonetheless, ChatGPT has a strong potential to increase human productivity in research and can be used in academic writing.
CONCLUSIONS: AI-generated text has the potential to improve the quality of high-impact research articles. The findings of this study suggest that decision makers and researchers should focus more on the methodology part of the research, which includes research design, developing research tools, and analyzing data in depth, to draw strong theoretical and practical implications, thereby establishing a revolution in scientific research in the era of AI. The practical implications of this study can be used in different fields such as medical education to deliver materials to develop the basic competencies for both medicine students and faculty members.