Abstract
Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified “healthy” and “moderate or severe” periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.
Highlights
Periodontitis is a multifactorial and multibacterial disease that occurs in the dental supporting tissues, involving a complex relationship between oral microorganisms organized in the subgingival biofilm and the homeostatic processes in the host
We analyzed 144 healthy controls and 548 chronic periodontitis patients and demonstrated that a multivariate machine learning algorithm based on nine salivary bacterial copy numbers is able to predict the severity of chronic periodontitis
Chen et al reported that four models classified the samples into healthy controls and periodontitis patients with
Summary
Periodontitis is a multifactorial and multibacterial disease that occurs in the dental supporting tissues, involving a complex relationship between oral microorganisms organized in the subgingival biofilm and the homeostatic processes in the host. Periodontitis has been shown to be associated with several other severe health issues, such as coronary artery disease (Humphrey et al, 2008), diabetes (Preshaw et al, 2012), premature birth (Walia and Saini, 2015), and rheumatoid arthritis (Araú jo et al, 2015). These associations have led to a change in the perception that the oral cavity is organically linked to the systemic physiology rather than just to an isolated organ (Sabharwal et al, 2018). Early detection and diagnosis of the disease is important because it may guarantee less-invasive, less-costly treatment than that of conventional dental care
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