Objectives: This study assessed the ability of ChatGPT, an artificial intelligence(AI) language model, to determine the stage, grade, and extent of periodontitis based on the 2018 classification. Materials and methods: This study used baseline digital data of 200 untreated periodontitis patients to compare standardized reference diagnoses (RDs) with ChatGPT findings and determine the best criteria for assessing stage and grade. RDs were provided by four experts who examined each case. Standardized texts containing the relevant information for each situation were constructed to query ChatGPT. RDs were compared to ChatGPT's responses. Variables influencing the responses of ChatGPT were evaluated. Results: ChatGPT successfully identified the periodontitis stage, grade, and extent in 59.5%, 50.5%, and 84.0% of cases, respectively. Cohen’s kappa values for stage, grade and extent were respectively 0.447, 0.284, and 0.652. A multiple correspondence analysis showed high variance between ChatGPT’s staging and the variables affecting the stage (64.08%) and low variance between ChatGPT’s grading and the variables affecting the grade (42.71%). Conclusions: The present performance of ChatGPT in the classification of periodontitis exhibited a reasonable level. However, it is expected that additional improvements would increase its effectiveness and broaden its range of functionalities (NCT05926999). Clinical relevance: Despite ChatGPT's current limitations in accurately classifying periodontitis, it is important to note that the model has not been specifically trained for this task. However, it is expected that with additional improvements, the effectiveness and capabilities of ChatGPT might be enhanced.

This study aims to compare scalpel and diode laser techniques regarding patients' perceptions, tissue healing, diastema, and periodontal clinical parameters in the treatment of abnormal labial frenum. This prospective, randomized, controlled trial evaluated 43 patients (aged 18-55) requiring labial frenectomy, randomized to scalpel or diode laser therapy. Plaque index (PI) and gingival index (GI) were measured at baseline, 4 weeks, and 6 months post-surgery. Keratinized gingiva width (KGW) of maxillary central incisors and diastemas were measured at baseline and 6 months post-surgery. Postoperative pain was evaluated on days 1, 7, 14, 21, and 28 using a visual analog scale. Wound healing was assessed at 7 days and 4 weeks postoperatively, scored based on the degree of epithelialization and the presence of ulceration or necrosis. At 6 months, both groups showed a significant reduction in PI, GI, and diastema (P<0.05). KGW increased in both groups, with a significant increase in the laser group (P<0.05), though baseline and 6-month KGW values were not significantly different between groups: baseline values were 5.30 ± 1.396 for the scalpel group and 5.05 ± 1.276 for the laser group, and 6-month values were 5.65 ± 1.152 for the scalpel group and 5.50 ± 1.147 for the laser group (P<0.05). The diode laser group had significantly lower pain scores than scalpel group on days 1, 3, and 7 (P<0.05). however, from day 14 onward, there was no statistically significant difference in pain scores between groups (P<0.05). Tissue healing was significantly faster on day 7 in the scalpel group (P<0.05). Frenectomy with diode laser effectively reduces pain, although it may delay wound healing. Laser therapy serves as a feasible alternative to the scalpel method. However, further research is necessary to fully assess its benefits and limitations in soft tissue procedures.

Objective: The objective of this study was to evaluate the effects of keratinized mucosa width (KMW) and mucosal thickness (MT) around dental implants on marginal bone loss (MBL). The evaluation was performed one year after loading by comparing clinical, radiographic, and biochemical parameters. Methods: The study included 87 implants in 87 patients undergoing regular follow-ups without hard or soft tissue augmentation one year after loading. Clinical measurements included plaque index (PI), gingival index (GI), bleeding on probing (BoP), probing depth (PD), KMW, and MT. MBL was assessed with periapical radiography. The peri-implant crevicular fluid (PICF) was analyzed for tumor necrosis factor-alpha (TNF-α), receptor activator of nuclear factor-kB ligand (RANKL), osteoprotegerin (OPG), and microRNA-27a. Results: The MBL of implants with thin MT (<2 mm) was higher than that of implants with thick MT (≥2 mm) (p < 0.05). A significant negative correlation (r: −0.217) was established between MT and MBL. No significant association was found between KMW and MBL (p > 0.05). No significant associations was found between KMW and MT with TNF-α, RANKL, OPG and RANKL/OPG (p > 0.05), with the exception of increased microRNA-27a levels in implants with KMW ≥ 2 mm (p < 0.05). Conclusions: Implants with a thick MT had a lower MBL. There may be an association between adequate KMW and high miRNA-27a levels. The relationship between MBL and miRNA-27a remains unclear.

Reduced sleep duration, poor sleep quality and fatigue are related to reduced immunity and increased inflammatory markers. Due to its potential to influence inflammation, poor sleep quality and fatigue could be factors for periodontitis and quality of life. Ninety-three individuals with untreated periodontitis and thirty-one individuals with healthy gingiva were included in the study. The research involved a clinical examination and a questionnaire. Demographic information, information on oral health, oral hygiene habits, the Pittsburgh Sleep Quality Index, Jenkins Sleep Scale, Multidimensional Assessment of Fatigue Scale, and Oral Health Impact Profile-14 were included in the questionnaire. Patients were diagnosed based on the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. No statistically significant difference was revealed between sleep quality, fatigue, oral health related quality of life, and stage-grade of periodontitis (p<0.05). However, periodontitis group had higher Oral Health Impact Profile-14 scores (p<0.05). A statistically significantly lower sleep duration was observed in stage IV periodontitis group than the other groups (p<0.05). A statistically significant positive correlation was observed between the Pittsburgh Sleep Quality Index and the scores of the other questionnaires (p<0.05). The stage of periodontitis may impact sleep duration.

The objective of this study is to evaluate the correlation between dietary patterns, levels of physical activity (PA), and the occurrence of periodontal disease. Many lifestyles have been connected to periodontal disease, but little is known about diet and PA intake. This study included 185 people—62 healthy, 61 gingivitis, and 62 periodontitis. PA, Mediterranean diet score (MDS), and carbohydrate consumption frequency were gathered together with demographics. Clinical and radiographic evaluations calculated decayed, missing, and filled teeth (DMFT) index scores. PA, MDS, carbohydrate consumption, and periodontitis were evaluated. Periodontitis patients had higher DMFT scores, poorer periodontal features, and less daily tooth brushing. More PA overall, more PA in free time, and less sedentary behavior reduced periodontitis rates. Sedentary behavior increased periodontal disease risk in adjusted univariate regression models (OR = 0.235; p = 0.021). Of the study participants, 35 (18.9%) had high MDS. No correlation was established between Mediterranean diet (MD) and periodontitis. Periodontitis and MD, DMFT, and carbohydrate consumption do not seem to be substantially correlated. According to our research, a higher risk of periodontal disease was linked to more sedentary activity. To clarify the extent of the relationship between PA and periodontal disease, more prospective longitudinal research and approaches are required. Clinical Relevance: The periodontal status of individuals can be influenced by their lifestyle.

Periodontal diseases could cause halitosis and may impair taste and smell. While non-surgical periodontal therapy is known to reduce halitosis, its effects on taste and smell are less studied. This study aims to investigate the factors influencing self-perceived halitosis, taste, and smell, as well as the changes in these perceptions after periodontal therapy. A total of 183 participants were divided into three groups: 61 patients with periodontitis, 61 with gingivitis, and 61 who were gingivally healthy. Periodontal parameters and self-perceived halitosis, taste, and smell were evaluated at baseline and four weeks after non-surgical periodontal treatment using a visual analog scale (VAS). Robust regression analysis was used to assess independent variables influencing baseline VAS ratings. The periodontitis group had the lowest taste perception and the highest self-perceived halitosis scores (p < 0.05). Taste perception was negatively associated with ≥ 4 mm pockets (p = 0.002). A positive relationship was also observed between the plaque index and self-perceived halitosis (p = 0.030). Post-treatment, taste perception improved significantly in all groups (p < 0.05), in parallel with improvements in periodontal parameters. Additionally, self-perceived halitosis showed a significant decrease (p < 0.05). The improvement in smell perception was statistically significant in the gingivally healthy and periodontitis groups (p < 0.05). Periodontal disease may contribute to the development of chemosensory disorders. While the main goal of periodontal treatment is disease management, it can also improve taste and smell function. Oral hygiene practices play an essential role in the development of these improvements. However, further research is needed on the subject. The study was registered as “Investigation of Halitosis, Taste, and Smell in Terms of Periodontal Condition Stated by Patients and Periodontal Diagnosis by Dentists, and Then Evaluation of Change Before and After Treatment” with the registration number NCT06063460 (13/09/2023) at https://www.clinicaltrials.gov Protocol Registration and Results System. This clinical trial was registered prior to participant recruitment on ClinicalTrials.gov (NCT06063460,13.09.2023).