Graph neural network-based prediction of all-cause and cardiovascular mortality using periodontal site-level data from National Health and Nutrition Examination Survey (NHANES)
Year: 2026
Type: article
Abstract: Background/purpose: Periodontitis is a common chronic inflammatory disease linked to systemic conditions. We applied a graph convolutional network (GCN) to site-level periodontal data to predict all-cause and cardiovascular mortality from National Health and Nutrition Examination Survey (NHANES). Materials and methods: Adults aged ≥30 years with full-mouth periodontal exams and linked mortality data through December 31, 2019 were included. Periodontal probing depth and clinical loss of attachment were measured. Each chart was converted into a graph with 168 nodes and anatomically defined edges. Graph-level embeddings were combined with age and sex to predict mortality. Model performance was evaluated in an independent test set using receiver operating characteristic - area under the curve (ROC AUC) and precision-recall - area under the curve (PR AUC). Results: < 0.001). Adjusted mortality probabilities rose from 2.2 % to 5.6 % across probing depth quartiles and from 2.2 % to 6.3 % across attachment loss quartiles. The GCN achieved strong discrimination, with ROC AUC = 0.831 and 0.845 and PR AUC = 0.397 and 0.203 for all-cause and cardiovascular mortality, respectively. Conclusion: A GCN applied to site-level periodontal data achieved strong discrimination in predicting mortality. This finding highlights the prognostic significance of periodontal health and demonstrates the potential of graph-based deep learning for modeling complex periodontal-systemic interactions.
Source: Journal of Dental Sciences
Language: English
Subfield: Periodontics
Field: Dentistry
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,600
OpenAlex ID: W7120087318