Performance of ChatGPT in classifying periodontitis according to the 2018 classification of periodontal diseases.

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.

Associations between immune-inflammatory markers, age, and periodontal status: a cross-sectional study.

Since periodontal disease is associated with many systemic diseases, it is important to evaluate its effects on host responses in elderly individuals. To this end, this study investigated salivary interleukin (IL)-17, IL-18, toll-like receptor (TLR) 2, TLR4, and tumor necrosis factor-alpha (TNF-α) levels in patient groups with different periodontal health statuses and immunologically evaluated the relationship between age and periodontal health status. A total of 60 individuals aged 18-40 years (young individuals) and 60 individuals aged 65 years or older (elderly individuals) were included in this study. According to periodontal disease status, the patients were divided into periodontally healthy, gingivitis, and periodontitis subgroups. Clinical periodontal parameters, including probing depth (PD), clinical attachment level (CAL), plaque index (PI), and gingival index (GI), were recorded. Saliva samples were collected and analyzed using ELISA to determine the levels of IL-17, IL-18, TLR2, TLR4, and TNF-α. Higher clinical periodontal parameter (PD, CAL, PI, and GI) and inflammatory marker (IL-17, IL-18, TNF-α, TLR2, and TLR4) levels were found in patients with periodontitis than those in periodontally healthy individuals and patients with gingivitis (P < 0.05). Salivary inflammatory marker levels were significantly higher in elderly individuals than those in young individuals in all subgroups (P < 0.05). A positive correlation was found between inflammatory marker levels and clinical periodontal parameters, but there was no correlation between TLR2 and PI or GI. This study suggests a significant increase in host response to periodontal disease as the disease progresses, with the levels of cytokines and TLR expression exhibiting an increasing trend with age. Increased IL-17, IL-18, TLR2, TLR4, and TNF-α levels in elderly individuals in all periodontal health subgroups might suggest the role of these cytokines and TLR pathway in the pathogenesis of periodontal diseases.

Potential Use of ChatGPT for Patient Information in Periodontology: A Descriptive Pilot Study.

Objectives The aim of this study is to evaluate the accuracy and completeness of the answers given by Chat Generative Pre-trained Transformer (ChatGPT) (OpenAI OpCo, LLC, San Francisco, CA), to the most frequently asked questions on different topics in the field of periodontology. Methods The 10 most frequently asked questions by patients about seven different topics (periodontal diseases, peri-implant diseases, tooth sensitivity, gingival recessions, halitosis, dental implants, and periodontal surgery) in periodontology were created by ChatGPT. To obtain responses, a set of 70 questions was submitted to ChatGPT, with an allocation of 10 questions per subject. The responses that were documented were assessed using two distinct Likert scales by professionals specializing in the subject of periodontology. The accuracy of the responses was rated on a Likert scale ranging from one to six, while the completeness of the responses was rated on a scale ranging from one to three. Results The median accuracy score for all responses was six, while the completeness score was two. The mean scores for accuracy and completeness were 5.50 ± 0.23 and 2.34 ± 0.24, respectively. It was observed that ChatGPT's responses to the most frequently asked questions by patients for information purposes in periodontology were at least "nearly completely correct" in terms of accuracy and "adequate" in terms of completeness. There was a statistically significant difference between subjects in terms of accuracy and completeness (P<0.05). The highest and lowest accuracy scores were peri-implant diseases and gingival recession, respectively, while the highest and lowest completeness scores were gingival recession and dental implants, respectively. Conclusions The utilization of large language models has become increasingly prevalent, extending its applicability to patients within the healthcare domain. While ChatGPT may not offer absolute precision and comprehensive results without expert supervision, it is apparent that those within the field of periodontology can utilize it as an informational resource, albeit acknowledging the potential for inaccuracies.

Effect of Maxillary Sinus Anatomy on Bone Gain After Lateral Window Sinus Floor Elevation: A Case-Control Study.

Purpose: To evaluate the effect of maxillary sinus anatomy on sinus floor elevation (SFE) operations performed with the lateral window approach. Materials and Methods: CBCT and digital panoramic radiographs were evaluated before and at least 6 months after maxillary sinus floor elevation (SFE) operations performed in 33 maxillary sinus regions in 26 patients. Maxillary sinus volume, grafted volume (GV), sinus width (SW), residual bone height (RBH), and vertical graft resorption (VGR) were calculated. The effects of RBH and SW on GV and VGR were evaluated statistically. Results: The mean GV values in the maxillary sinuses classified as narrow, average, and wide were 2.60 ± 0.57, 3.44 ± 0.65, and 3.70 ± 0.64 cm3, respectively. The mean VGR was 2.12 ± 1.67 mm, and in the sites classified as narrow, average, and wide, the mean VGR values were 1.50 ± 0.79, 1.58 ± 1.28, and 3.46 ± 2.06 mm, respectively. A statistically significant difference in GV and VGR was found between the SW groups. The mean posterior maxillary RBH was 2.30 ± 1.05 mm, and 17 and 16 sinuses were classified as ≤ 2 and > 2 mm, respectively. There was no statistically significant difference in the effect of RBH on GV or VGR. Conclusion: Although posterior maxillary RBH did not affect new bone formation in the sinuses grafted only with hydroxyapatite-derived inorganic bovine bone, with increasing SW, the GV decreased and the VGR increased.