RESUMO
BACKGROUND: With recent advances in artificial intelligence, the use of this technology has begun to facilitate comprehensive tissue evaluation and planning of interventions. This study aimed to assess different convolutional neural networks (CNN) in deep learning algorithms to detect keratinized gingiva based on intraoral photos and evaluate the ability of networks to measure keratinized gingiva width. METHODS: Six hundred of 1200 photographs taken before and after applying a disclosing agent were used to compare the neural networks in segmenting the keratinized gingiva. Segmentation performances of networks were evaluated using accuracy, intersection over union, and F1 score. Keratinized gingiva width from a reference point was measured from ground truth images and compared with the measurements of clinicians and the DeepLab image that was generated from the ResNet50 model. The effect of measurement operators, phenotype, and jaw on differences in measurements was evaluated by three-factor mixed-design analysis of variance (ANOVA). RESULTS: Among the compared networks, ResNet50 distinguished keratinized gingiva at the highest accuracy rate of 91.4%. The measurements between deep learning and clinicians were in excellent agreement according to jaw and phenotype. When analyzing the influence of the measurement operators, phenotype, and jaw on the measurements performed according to the ground truth, there were statistically significant differences in measurement operators and jaw (p < 0.05). CONCLUSIONS: Automated keratinized gingiva segmentation with the ResNet50 model might be a feasible method for assisting professionals. The measurement results promise a potentially high performance of the model as it requires less time and experience. PLAIN LANGUAGE SUMMARY: With recent advances in artificial intelligence (AI), it is now possible to use this technology to evaluate tissues and plan medical procedures thoroughly. This study focused on testing different AI models, specifically CNN, to identify and measure a specific type of gum tissue called keratinized gingiva using photos taken inside the mouth. Out of 1200 photos, 600 were used in the study to compare the performance of different CNN in identifying gingival tissue. The accuracy and effectiveness of these models were measured and compared to human clinician ratings. The study found that the ResNet50 model was the most accurate, correctly identifying gingival tissue 91.4% of the time. When the AI model and clinicians' measurements of gum tissue width were compared, the results were very similar, especially when accounting for different jaws and gum structures. The study also analyzed the effect of various factors on the measurements and found significant differences based on who took the measurements and jaw type. In conclusion, using the ResNet50 model to identify and measure gum tissue automatically could be a practical tool for dental professionals, saving time and requiring less expertise.
RESUMO
The BPA into wastewater has posed a threat to environment and human health. Hence, we aimed to eliminate BPA in a short time and with a rapid degradation rate from food wastewater. Herein, the effects of different alkaline-earth oxide doped with Bi2O3 nanoparticles on the photocatalytic degradation of bisphenol A were investigated. SrO-Bi2O3, CaO-Bi2O3, and MgO-Bi2O3 binary oxides were prepared by wet-impregnation method. The structural and optical features of catalysts were clarified BET, XRD, DRS, FT-IR, PL, and SEM techniques. The photocatalytic activities of catalysts were compared for different light sources. Considering that the characterization analysis and experimental results, the highly improved photocatalytic activity was mainly attributed to the effective structure of the SrO-Bi2O3 binary oxide and the strong alkali properties in the nanocomposite. Obviously, 5wt% SrO-Bi2O3 photocatalyst showed more excellent degradation performance and highest degradation reaction rate (0.21 mg l- 1 min- 1) within 30 min. It was observed that the photocatalytic activity improved by the additive of alkaline oxide on Bi2O3.
