Performance of AI chatbots on controversial topics in oral medicine, pathology, and radiology.

OBJECTIVES: In this study, we assessed 6 different artificial intelligence (AI) chatbots (Bing, GPT-3.5, GPT-4, Google Bard, Claude, Sage) responses to controversial and difficult questions in oral pathology, oral medicine, and oral radiology. STUDY DESIGN: The chatbots' answers were evaluated by board-certified specialists using a modified version of the global quality score on a 5-point Likert scale. The quality and validity of chatbot citations were evaluated. RESULTS: Claude had the highest mean score of 4.341 ± 0.582 for oral pathology and medicine. Bing had the lowest scores of 3.447 ± 0.566. In oral radiology, GPT-4 had the highest mean score of 3.621 ± 1.009 and Bing the lowest score of 2.379 ± 0.978. GPT-4 achieved the highest mean score of 4.066 ± 0.825 for performance across all disciplines. 82 out of 349 (23.50%) of generated citations from chatbots were fake. CONCLUSIONS: The most superior chatbot in providing high-quality information for controversial topics in various dental disciplines was GPT-4. Although the majority of chatbots performed well, it is suggested that developers of AI medical chatbots incorporate scientific citation authenticators to validate the outputted citations given the relatively high number of fabricated citations.

Effect of mechanical forces on the behavior of osteoblasts: a systematic review of in vitro studies.

Mechanical loading can play a critical role in bone modeling/remodeling through osteoblasts, with several factors being involved in this process.The present study aims to systematically review the effect of mechanical stimulation on human osteoblast cell lineage combined with other variables.The PubMed and Scopus databases were electronically searched for studies analyzing the effect of compression and tension on human osteoblasts at different differentiation stages. Studies that used carcinogenic osteoblasts were excluded. In addition, studies that did not analyze the osteogenic differentiation or proliferation of cells were excluded. The risk of bias of the studies was evaluated using the modified CONSORT (Consolidated Standards of Reporting Trials) checklist. a total of 20 studies were included. The cells were subjected to tension and compression in 5 and 15 studies, respectively. The application of uniaxial and cyclic strain increased the proliferation of osteoblasts. The same increased pattern could be observed for the osteogenesis of the cells. The impact of the tensile force on the expression of the osteoclastic markers differed based on the loading characteristics. On the other side, the impact of compression on the proliferation of osteoblasts varied according to the magnitude and duration of the force. Besides, different patterns of alternations were observed among the osteogenic markers in response to compression. Meanwhile, compression increased the expression of the osteoclastic markers. It has been shown that the response of the markers related to bone formation or resorption can be altered based on the differentiation stage of the cells, the cell culture system, and the magnitude and duration of the force.

Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy.

INTRODUCTION: The aim of this systematic review and meta-analysis was to investigate the overall accuracy of deep learning models in detecting periapical (PA) radiolucent lesions in dental radiographs, when compared to expert clinicians. METHODS: Electronic databases of Medline (via PubMed), Embase (via Ovid), Scopus, Google Scholar, and arXiv were searched. Quality of eligible studies was assessed by using Quality Assessment and Diagnostic Accuracy Tool-2. Quantitative analyses were conducted using hierarchical logistic regression for meta-analyses on diagnostic accuracy. Subgroup analyses on different image modalities (PA radiographs, panoramic radiographs, and cone beam computed tomographic images) and on different deep learning tasks (classification, segmentation, object detection) were conducted. Certainty of evidence was assessed by using Grading of Recommendations Assessment, Development, and Evaluation system. RESULTS: A total of 932 studies were screened. Eighteen studies were included in the systematic review, out of which 6 studies were selected for quantitative analyses. Six studies had low risk of bias. Twelve studies had risk of bias. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of included studies (all image modalities; all tasks) were 0.925 (95% confidence interval [CI], 0.862-0.960), 0.852 (95% CI, 0.810-0.885), 6.261 (95% CI, 4.717-8.311), 0.087 (95% CI, 0.045-0.168), and 71.692 (95% CI, 29.957-171.565), respectively. No publication bias was detected (Egger's test, P = .82). Grading of Recommendations Assessment, Development and Evaluationshowed a "high" certainty of evidence for the studies included in the meta-analyses. CONCLUSION: Compared to expert clinicians, deep learning showed highly accurate results in detecting PA radiolucent lesions in dental radiographs. Most studies had risk of bias. There was a lack of prospective studies.

Comparison of Periodontal Ligament Stem Cells with Mesenchymal Stem Cells from Other Sources: A Scoping Systematic Review of In vitro and In vivo Studies.

OBJECTIVE: The application of stem cells in regenerative medicine depends on their biological properties. This scoping review aimed to compare the features of periodontal ligament stem cells (PDLSSCs) with stem cells derived from other sources. DESIGN: An electronic search in PubMed/Medline, Embase, Scopus, Google Scholar and Science Direct was conducted to identify in vitro and in vivo studies limited to English language. RESULTS: Overall, 65 articles were included. Most comparisons were made between bone marrow stem cells (BMSCs) and PDLSCs. BMSCs were found to have lower proliferation and higher osteogenesis potential in vitro and in vivo than PDLSCs; on the contrary, dental follicle stem cells and umbilical cord mesenchymal stem cells (UCMSCs) had a higher proliferative ability and lower osteogenesis than PDLSCs. Moreover, UCMSCs exhibited a higher apoptotic rate, hTERT expression, and relative telomerase length. The immunomodulatory function of adipose-derived stem cells and BMSCs was comparable to PDLSCs. Gingival mesenchymal stem cells showed less sensitivity to long-term culture. Both pure and mixed gingival cells had lower osteogenic ability compared to PDLSCs. Comparison of dental pulp stem cells (DPSCs) with PDLSCs regarding proliferation rate, osteo/adipogenesis, and immunomodulatory properties was contradictory; however, in vivo bone formation of DPSCs seemed to be lower than PDLSCs. CONCLUSION: In light of the performed comparative studies, PDLSCs showed comparable results to stem cells derived from other sources; however, further in vivo studies are needed to determine the actual pros and cons of stem cells in comparison to each other.

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.

COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

Quality Assessment of Studies Included in Cochrane Oral Health Systematic Reviews: A Meta-Research.

OBJECTIVES: To assess the Risk of Bias (RoB) and other characteristics of published randomised clinical trials within Cochrane oral health systematic reviews. MATERIALS AND METHODS: All the published clinical trials within Cochrane oral health systematic reviews until 1 June 2020 were identified and examined. RoB was assessed for all the included clinical trials according to the Cochrane review standards. The Overall Risk of Bias (ORoB) was defined in this study using Cochrane's RoB tool-v2. Descriptive analyses were carried out to determine the frequency of each variable in the study sample. RESULTS: Out of a total of 2565 included studies, the majority (n = 1600) had sample sizes of 50 or higher. Regarding blinding, 907 studies were labelled as double-blind. Among the various domains of bias, the performance bias showed the highest rate of high risk (31.4%). Almost half of the studies had a high ORoB, compared to 11.1% with a low ORoB. The studies that used placebos had a higher percentage of low ORoB (14.8% vs. 10.7%). Additionally, the double- and triple-blind studies had higher percentages of low ORoB (23.6% and 23.3%, respectively), while the studies with a crossover design had the highest percentage of low ORoB (28.8%). CONCLUSION: The RoB of oral health studies published as Cochrane reviews was deemed high.

Role of ACE2 polymorphism in COVID-19: impact of age.

More than 2 million people have died as a result of the COVID-19 outbreak. Angiotensin-converting enzyme 2 (ACE2) is a counter-regulatory enzyme that converts angiotensin-2 to Ang-(1-7) form in the renin-angiotensin system. Several studies have been analyzed the correlation between ACE2 and COVID-19. Indeed, ACE2/Ang (1-7) system protects the lung against acute respiratory distress syndrome by its anti-inflammatory/anti-oxidant function. However, SARS-Cov-2 can use ACE2 for host cell entry. Expression of ACE2 can be altered by several factors, including hypertension, diabetes and obesity, which also could increase the severity of COVID-19 infection. Besides, since androgens increase the expression of ACE-2, males are at higher risks of COVID-19 infection. Although reported statistics showed a significantly different infection risks of COVID-19 between adults and children, the reason behind the different responses is still unclear. This review proposes the effect of ACE polymorphism on the severity of SARS-COV-2 induced pneumonia. The previous meta-analysis regarding the effect of ACE polymorphism on the severity of pneumonia showed that polymorphism only affects the adult's illness severity and not the children. Two recent meta-analyses examined the effect of ACE polymorphism on the prevalence and mortality rate of COVID-19 and reported contradicting results. Our opinion paper suggests that the effect of ACE polymorphism on the severity of COVID-19 depends on the patients age, same as of the pneumonia.