Periodontics and Periodontology Commons^™

The Clinical Potential Of Cone-Beam Computed Tomography Image Segmentation In Dentistry, Hsin-Hua Chou, Takashi Saito, Chih-Kai Huang, Wei-Chun Lin

Journal of Dental Sciences

No abstract provided.

Oral Pathogens Reprogram Endothelial Barrier And Angiotensin-Converting Enzyme2/Endothelial Nitric Oxide Synthase-Nitric Oxide Signaling To Promote Blood Pressure Dysregulation, Ming-Shou Hsieh, Tung-Nien Hsu, Chin-Sheng Huang, Ting-Yi Renn, Kuang-Tai Kuo, Kuo-Yao Lin, Wei Jen Chang

Journal of Dental Sciences

Background/purpose: Although periodontal disease is associated with an increased risk of hypertension, the endothelial mechanisms linking oral inflammation to vascular dysfunction remain unclear. We investigated whether Porphyromonas gingivalis lipopolysaccharide (Pg-LPS) can directly disturb endothelial homeostasis and whether macrophage–endothelial crosstalk further worsens endothelial injury.

Materials and methods: Human umbilical vein endothelial cells (HUVECs) were exposed to Pg-LPS alone or co-cultured (transwell system) with Pg-LPS-primed human monocytic cells -derived macrophages. Endothelial dysfunction was assessed through nitric oxide (NO) production, endothelial nitric oxide synthase (eNOS) phosphorylation, inflammatory and adhesion molecule expression, as well as junctional and cytoskeletal changes. Macrophage …

Ensemble Learning For Predicting Periapical Lesions From Dental Radiographs, Fang-Yuan Liao, Ming-Jyun Jian, Abdullah Sarini, Yulianto Jesica, Yen-Kun Lin, Hsun-Yu Huang

Journal of Dental Sciences

Background/purpose: Diagnosing periapical cysts and granulomas using periapical radiographs is challenging due to subtle radiographic differences. This study aimed to develop a machine learning–based diagnostic framework to improve lesion classification through ensemble learning and dentin standardization.

Materials and methods: Five models—Support Vector Classifier (SVC), Nu-SVC, K-Nearest Neighbors (KNN) and Decision Tree—were trained on 144 pre-treatment periapical radiographs (70 cysts and 74 granulomas). Dentin standardization normalized grayscale values to ensure feature consistency. A weighted soft voting strategy was employed to integrate predictions, with model weights derived from individual diagnostic performance. Model performance was evaluated via five-fold cross-validation. Statistical significance among models …

Development And Clinical Validation Of An Artificial Intelligence–Assisted Radiographic System For Predicting Periodontal Pocket Severity Using Full-Mouth Radiographs., Yi-Tai Ho, Tsui-Hsien Huang, Cheng-Han Shie, Tao-Wei Chang, Chun-Te Ho, Chia-Tze Kao

Journal of Dental Sciences

Background/purpose: Conventional periodontal probing is the gold standard for evaluating periodontal disease severity; however, it is time-consuming and susceptible to operator-dependent variability. This study developed and clinically validated an artificial intelligence (AI)-assisted radiographic system for automated periodontal disease severity assessment using full-mouth radiographs, and investigated its association with clinical periodontal probing depth.

Materials and methods: A total of 655 full-mouth radiographs were retrospectively collected: 151 (3,962 teeth) for AI model training and 504 (13,809 teeth) for validation. Periodontal bone loss was quantified using the periodontal bone loss (PBL) ratio and classified as mild, moderate, or severe. Clinical validation used 31 …