Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. In this study, a total of 134 intraoral images were divided into a training dataset (n = 107 [80%]) and a test dataset (n = 27 [20%]). Two Faster Region-based Convolutional Neural Network (R-CNN) models using ResNet-50 Convolutional Neural Network (CNN) were developed. The first model detects the teeth to locate the region of interest (ROI), while the second model detects gingival inflammation. The detection accuracy, precision, recall, and mean average precision (mAP) were calculated to verify the significance of the proposed model. The teeth detection model achieved an accuracy, precision, recall, and mAP of 100 %, 100%, 51.85%, and 100%, respectively. The inflammation detection model achieved an accuracy, precision, recall, and mAP of 77.12%, 88.02%, 41.75%, and 68.19%, respectively. This study proved the viability of deep learning models for the detection and diagnosis of gingivitis in intraoral images. Hence, this highlights its potential usability in the field of dentistry and aiding in reducing the severity of periodontal disease globally through preemptive non-invasive diagnosis.

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Absorbable sutures were preferred in the majority of periodontal procedures; however, non-absorbable sutures were favored in procedures that required longer healing or better stability of the flap edges in cases of periodontal and ridge augmentation.

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Background: Chlorhexidine oral rinse has been used as an adjuvant in the treatment of periodontal disease. However, there are drawbacks of using chlorhexidine i.e. tooth staining and other side effects, including allergy reaction. In light of the proven therapeutic properties of pollen water as well as its relatively cheap cost in the market, pollen water has a potential to be an effective alternative to chlorhexidine oral rinse. The aim of this study is to compare the degree of tooth staining influenced by water-based pollen mouthwash to the standard Chlorhexidine mouthwash using spectrophotometer. Materials and Methods: 24 specimens from extracted intact human teeth were soaked into the three different solutions, Chlorhexidine, Pollen water (date palm pollen water suspension), and normal water. Color measurements were carried out by a spectrophotometer devise and recorded at 5 different time intervals. Color change (∆E), Chroma (C*) and Hue (H*) were analyzed and compared among the three solutions. Results: Overall mean ∆E was similar in all groups, significant difference between all time points was found only in pollen water. The change in C* was higher in pollen water as compared to other solutions. There was a subtle increase in H* in the Chlorhexidine samples after week 3. The H* values in pollen water were stable, but a sudden decrease was observed in week 6. The difference in H* among the three solutions was significant after 3 weeks. Conclusion: Within the limitation of our study, it can be concluded that Pollen water stained teeth to a lesser extent than did chlorhexidine. It might be beneficial to use Pollen water as mouthwash however, further investigation is needed regarding the efficacy of plaque control.