RESUMEN
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.
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BACKGROUND: Flurbiprofen which is a non-steroidal anti-inflammatory drug (NSAID), has been safely used for the control of postoperative patient's morbidity after periodontal plastic surgeries requiring palatal graft harvesting, but there is little information on the efficacy of topical use. The aim of the study was to evaluate whether patient pain perception was reduced and patient morbidity was improved by using oral spray of flurbiprofen after palatal graft harvesting. METHODS: Forty-eight patients (21 males and 27 females), scheduled for subepithelial connective tissue graft (SCTG) and free gingival graft (FGG) requiring periodontal plastic surgeries were selected. The patients were randomly assigned to each group and used oral spray of flurbiprofen or placebo three times a day for a week. The palatal donor area was evaluated at 1, 3, 7, 14, 21, 28, 42, and 56-day follow-up after the surgery for postoperative pain, patients' discomfort, complete epithelialization, changes in dietary habits, burning sensation, color match, the amount of systemic analgesic consumption and the presence of delayed bleeding. Wound healing scores were recorded at 14-day follow up. RESULTS: The prevalance of complete epithelialization was significantly higher in the placebo-FGG group than flurbiprofen-FGG group at 21 days postoperatively (P < 0.05), while there was no significant alteration for both flurbiprofen-SCTG and placebo-SCTG groups at any follow-up periods. In flurbiprofen-FGG group, significant improvements were observed for postoperative pain, patients' discomfort and burning sensation at 14 days postoperatively (P < 0.05). CONCLUSION: Oral flurbiprofen spray reduces patient's morbidity, however it might have negative effects on epithelialization of secondary wound healing after FGG operations.