The aim of the study is to evaluate the effectiveness of different toothbrushes in reducing gingival inflammation and dental biofilm removal in gingivitis patients with fixed orthodontic appliances. Following baseline clinical assessments, including the Turesky modification of the Quigley-Hein plaque index (TQHI), gingival index (GI), bleeding on probing (BOP), and probing pocket depth (PPD), all patients received nonsurgical periodontal therapy. Patients were then randomly assigned to one of three groups: conventional toothbrush (C-TB), orthodontic toothbrush (O-TB), or single-tufted toothbrush (ST-TB). Each group received tailored oral hygiene instructions based on their assigned toothbrush type. Clinical assessments were repeated and recorded in the first week, sixth week, and third month. Thirty-six patients with a mean age of 23.14 ± 3.86 (18–32 years) participated in the study. All groups with twelve patients each demonstrated significant improvements in clinical parameters compared to baseline. In the sixth week, the O-TB and ST-TB groups showed significantly greater improvements in TQHI and BOP scores compared to the C-TB group (p < 0.05). By the third month, the ST-TB group maintained significantly lower TQHI and BOP scores, while the O-TB group’s scores became similar to those of the C-TB group. ST-TB and C-TB groups had significantly lower GI scores than the O-TB group in all time points. Although the effectiveness of ST-TB was significantly higher than that of other toothbrushes, all toothbrushes demonstrated lower efficacy in the posterior region. The ST-TB was shown to be more effective in reducing gingival inflammation over three months, while it demonstrated similar efficacy to the O-TB in dental biofilm removal. Further research is necessary to substantiate these findings and to demonstrate the efficacy of this approach in promoting periodontal tissue health in patients with fixed orthodontic appliances. This study was registered with the National Library of Medicine Clinical Trials Registry Platform on 19/07/2024 as NCT06510179.

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.

Background: The determination of the impact of risk factors such as smoking in periodontal disease development is of importance to better characterize the disease. However, its impact on host response remains unclear. This study aimed to evaluate the effects of tobacco smoking on GCF levels of neutrophil enzymes (myeloperoxidase (MPO), beta-glucuronidase (BGD), neutrophil elastase (NE) and periodontal parameters in healthy young adults with dental plaque biofilm-induced gingivitis. Methods: The study population consisted of 60 systemically healthy young adults (39 smokers (Sm) and 21 non-smokers (n-Sm)) diagnosed with plaque-induced gingivitis. The periodontal examination consisted of a plaque index (PI); gingival index (GI); probing depth (PD); bleeding on probing (BoP), and clinical attachment level (CAL). GCF MPO, BGD, and NE levels were determined by means of an enzyme-linked immunosorbent assay (ELISA). Results: PI, GI, and BoP were significantly increased in the Sm group (p < 0.05). PD and CAL showed no significant difference between Sm and n-Sm groups (p > 0.05). In GCF, MPO, BGD, and NE levels were significantly increased in Sm group (p < 0.05). NE levels showed a significant correlation with GI and BoP (p < 0.05 for both). Moreover, a positive correlation between BGD and NE levels (p < 0.05) was measured. Conclusions: It may be concluded that, even in young patients, tobacco consumption affects the host’s immune response related to gingival inflammation. It is, therefore, mandatory to inform young patients about the risk related to tobacco consumption for their gingival health.

The aim of our study was to obtain similar surface properties and elemental composition to virgin implants after debridement of contaminated titanium implant surfaces covered with debris. Erbium-doped:yttrium, aluminum, and garnet (Er:YAG) laser, erbium, chromium-doped:yttrium, scandium, gallium, and garnet (Er,Cr:YSGG) laser, curette, and ultrasonic device were applied to contaminated implant surfaces. Scanning electron microscopy (SEM) images were taken, the elemental profile of the surfaces was evaluated with energy dispersive X-ray spectroscopy (EDX), and the surface roughness was analyzed with profilometry. Twenty-eight failed implants and two virgin implants as control were included in the study. The groups were designed accordingly; titanium curette group, ultrasonic scaler with polyetheretherketone (PEEK) tip, Er: YAG very short pulse laser group (100 μs, 120 mJ/pulse 10 Hz), Er: YAG short-pulse laser group (300 μs, 120 mJ/pulse, 10 Hz), Er: YAG long-pulse laser group (600 μs, 120 mJ/pulse, 10 Hz), Er, Cr: YSGG1 laser group (1 W 10 Hz), Er, Cr: YSGG2 laser group (1.5 W, 30 Hz). In each group, four failed implants were debrided for 120 s. When SEM images and EDX findings and profilometry results were evaluated together, Er: YAG long pulse and ultrasonic groups were found to be the most effective for debridement. Furthermore, the two interventions have shown the closest topography of the sandblasted, large grit, acid-etched implant surface (SLA) as seen on virgin implants.

Artificial intelligence has increasingly influenced the field of periodontology by enhancing diagnostic accuracy and treatment planning through advanced data-driven techniques. It was aimed to examine the integration of artificial intelligence, particularly deep learning and machine learning, in analyzing intraoral photographs for periodontal conditions in this review. Periodontal assessments rely on clinical and radiographic evaluations, but artificial intelligence introduces a transformative approach by analyzing large datasets to improve clinical decision-making. The review investigates the effectiveness of artificial intelligence-enhanced intraoral photograph analysis, focusing on methodologies for dataset creation, model development, training, and performance evaluation. A thorough search of databases such as PubMed, Scopus, Google Scholar, and IEEE Xplore identified 338 articles, with 16 meeting the inclusion criteria. These studies primarily utilized convolutional neural networks and architectures like DeepLabv3+ and U-Net, demonstrating high accuracy in detecting conditions such as gingivitis, dental plaque, and other periodontal issues. The dataset sizes ranged from 110 to 7220 images, affecting the models' generalizability. Most studies employed supervised learning, with models trained on labeled datasets to achieve precise diagnostic outcomes. The review highlights that while artificial intelligence and machine learning techniques, including convolutional neural networks and U-Net, offer significant improvements in periodontal diagnostics, the choice of model and the quality of the dataset are crucial for performance. Hybrid approaches that combine automated and expert-driven methods might provide a balance between efficiency and accuracy. The successful integration of artificial intelligence into clinical practice requires continuous validation and adaptation to ensure that these technologies remain accurate and relevant. Future research should focus on enhancing model robustness, expanding dataset diversity, and refining clinical applications to fully exploit the potential of artificial intelligence in periodontology.