Classification success of salivary interleukin-1β in periodontitis grading with artificial intelligence models: a cross-sectional observational study

Abstract

Objectives  Finding certain biomarkers and threshold values of periodontitis and incorporating them into classifications can further highlight its impact on systemic health. This cross-sectional observational study aims to evaluate the efficacy of some biomarkers in grading periodontitis using artificial intelligence (AI) models. Methodology  AI models were developed to automatically classify periodontal status (N=240) and grades in periodontitis patients (n=120) using Python based on sociodemographic, anthropometric, clinical, radiological, and biochemical attributes. A total of 35 serum levels of whole blood attributes (white blood cell (WBC), platelet, erythrocyte, neutrophil, lymphocyte counts, and mean platelet volume), lipid profile [triglycerides; high-, low-, and very low-density lipoproteins (HDL, LDL, VLDL), and total cholesterol levels], salivary and serum interleukin (IL)-1β and matrix metalloproteinase (MMP)-8 levels), and 11 other attributes were used in the current classification. Results  In total, 23 out of 46 attributes achieved a 0.967 classification accuracy, whereas nine, a 0.858 classification accuracy. Attributes such as WBC, serum IL- 1β, triglyceride/HDL ratio, neutrophil/lymphocyte ratio, and HDL were instrumental in periodontal status classification. HDL, LDL, neutrophil/lymphocyte ratio, total cholesterol, salivary IL-1β, and MMP-8 were key attributes in grading. Conclusions  AI models showed significant classification accuracy, particularly with serum and salivary IL-1β levels and other blood parameters, underscoring the potential of these biomarkers, which could be integrated into the current classification.