Background and Objective Our recent work shows that periodontitis experience reflects host susceptibility to the onset of multiple systemic diseases and conditions. This cross‐sectional study further investigated whether and to what extent the existing periodontitis could reflect the concurrent presence of inflammatory comorbidities among ‘self‐perceived health’ individuals. Materials and Methods There were 115 ‘self‐perceived health’ adults who completed a questionnaire on demographic characteristics and lifestyles. Twenty medical diagnostic tests were then performed to detect eight common systemic diseases and conditions. Meanwhile, full‐mouth periodontal examination was undertaken, and the subjects were classified as two subgroups with or without Generalized Severe Periodontitis (Stages III/IV, generalized). The interlink of periodontal status and concurrent systemic comorbidities was assessed. Results 98.3% (113/115) of the subjects exhibited at least one undiagnosed systemic disease/disorder. Of them, 52.2% (59/113) and 47.8% (54/113) concurrently presented with 1–5 or ≥6 abnormal test results, respectively. Overall, 96.5% (111/115) had periodontitis. Generalized Severe Periodontitis was present in 43.2% (48/111) of the periodontitis patients, and it was significantly associated with the profiles of abnormal test results after adjusting potential confounders (abnormal test results 1–5 vs ≥6; OR: 3.23, p = .012). Conclusions The present study shows that existing severe periodontitis could well reflect the concurrent presence of multiple inflammatory comorbidities. Oral and medical professionals can play proactive roles in enhancing health awareness and healthcare, through strong collaboration and teamwork.

Salivary ACE2 well reflected the severity of periodontal diseases, while salivary TMPRSS2 could link to some medical testing results relating to obesity and heart disease. Our findings imply the potential application of the biofluidic soluble form of SARS-CoV-2 entry proteins in assessing oral or systemic conditions and delivering appropriate healthcare.

Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90–94), 0.87 (95% CI, 0.84–89) and 0.78 (95% CI, 0.75–81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.