Objective: To evaluate the association between alveolar bone loss (ABL) detected on panoramic radiographs and carotid artery calcification (CAC) detected on computed tomography (CT). Methods: The study subjects included 295 patients (mean age ± SD: 64.6 ± 11.8 years) who visited the Matsumoto Dental University Hospital. The rate of ABL and the number of present teeth were measured on panoramic radiographs. Univariate analyses with t-tests and chi-squared tests were performed to evaluate the differences in age, gender, history of diseases, number of present teeth, and the ABL between subjects, with and without CAC. Moreover, multivariate logistic regression analysis, with forward selection and receiver operating characteristic curve (ROC) analysis, was performed. Results: The number of subjects without and with CAC was 174 and 121, respectively. Univariate analyses revealed that CAC was significantly associated with age, hypertension, osteoporosis, number of present teeth, and ABL. Multivariate logistic regression analysis adjusted for covariates revealed that the presence of CAC was significantly associated with ABL (OR = 1.233, 95% CI = 1.167–1.303). In the ROC analysis for predicting the presence of CAC, the the area under the ROC curve was the highest at 0.932 (95% CI = 0.904–0.960) for ABL, which was significant. Conclusions: Our results suggest that the measurement of ABL on panoramic radiographs may be an effective approach to identifying patients with an increased risk of CAC.

Background: to evaluate the association between type 2 diabetes and periodontal disease severity using the rate of alveolar bone loss (ABL) and high-sensitivity C-reactive protein (hs-CRP) value as indices. Methods: In this cross-sectional study of 372 patients (mean age ± SD, 53.2 ± 11.8 years) from a Japanese hospital, we measured ABL and number of teeth on either panoramic radiographs or intraoral dental radiographs of all teeth. Periodontal disease severity was classified into nine groups by combining ABL and hs-CRP. Results: 48 subjects had type 2 diabetes; 324 did not. Univariate analysis showed that type 2 diabetes was significantly associated with age, sex, body mass index, number of teeth, ABL, hs-CRP, and periodontal disease severity. Multivariate analysis showed significant associations between type 2 diabetes and the groups with high severity of periodontal disease. In receiver operating characteristic (ROC) curve analysis, predicting the presence of diabetes, area under the ROC curve was 0.762 (95%CI = 0.688–0.835) for ABL, and 0.709 (95%CI = 0.635–0.784) for hs-CRP, which was significant. Conclusions: this study showed that diabetes can be associated with a periodontal disease severity classification using the combination of ABL and hs-CRP.

Background: To evaluate the association between type 2 diabetes and periodontal disease severity using the rate of alveolar bone loss (ABL) and high-sensitivity C-reactive protein (hs-CRP) value as indices. Methods: In this cross-sectional study of 372 subjects (mean age ± SD, 53.2 ± 11.8 years) from a Japanese hospital, we measured ABL and number of teeth on either panoramic radiographs or intraoral dental radiographs of all teeth. A trial classification of periodontal disease was defined as follows: I (ABL < 25%, clinically mild); II (ABL ≥ 25%, <35%, moderate); III (ABL ≥ 35%, severe); and A (hs-CRP < 440 ng/ml, mild inflammation); B (hs-CRP ≥440 ng/ml, <1,020 ng/ml, moderate inflammation); and C (hs-CRP ≥ 1,020 ng/ml, severe inflammation). Periodontal disease severity was classified into nine groups by combining ABL and hs-CRP. Results: Forty-eight subjects had type 2 diabetes; 324 did not. Univariate analysis showed that type 2 diabetes was significantly associated with age, sex, body mass index, number of teeth, ABL, hs-CRP, and periodontal disease severity. Multivariate analysis showed significant associations between type 2 diabetes and the groups with high severity of periodontal disease. In receiver operating characteristic (ROC) curve analysis predicting the presence of diabetes, area under the ROC curve was 0.762 (95%CI = 0.688 - 0.835) for ABL, and 0.709 (95%CI = 0.635 - 0.784) for hs-CRP, which was significant. Conclusions: This study showed that diabetes could be associated with a periodontal disease severity classification using the combination of ABL and hs-CRP.

Large language models (LLMs) show varying accuracy on professional examinations, particularly for questions involving visual materials. This study compared GPT-4o, o3, GPT-5 Thinking, and Gemini 3 on 400 Japanese National Dental Technician Examination questions from 2018–2022 and investigated whether between-model differences varied by question format. The dataset comprised 252 text-only questions and 148 questions accompanied by visual materials. Accuracy was compared using Cochran’s Q test and Holm-adjusted exact McNemar tests. A question-level mixed-effects logistic regression assessed model-by-item-type interactions while accounting for examination year. Overall accuracy was 63.8% for GPT-4o, 70.8% for o3, 74.8% for GPT-5 Thinking, and 80.5% for Gemini 3 (Cochran’s Q, p < 0.001). Gemini 3 significantly outperformed the other models, whereas o3 and GPT-5 Thinking did not differ significantly after adjust-ment. All models showed lower accuracy on visual-material questions than on text-only questions. The overall model-by-item-type interaction was significant (likelihood-ratio χ² = 11.756, p = 0.008), indicating that between-model differences varied by question format. The relative advantages of GPT-5 Thinking and Gemini 3 over GPT-4o were at-tenuated for visual-material questions. These findings highlight format-dependent dif-ferences in LLM performance and the limitations of aggregate accuracy as a bench-marking metric.