The accurate diagnosis of individual tooth prognosis has to be determined comprehensively in consideration of the broader treatment plan. The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive treatment planning curriculum (CTPC). The tooth prognosis of 2359 teeth from 94 cases was evaluated with 1 to 5 levels (1—Hopeless, 5—Good condition for long term) by two groups (Model-A with 16, and Model-B with 13 examiners) based on 17 clinical determining factors selected from the HSDM-CTPC. Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm. These three methods were evaluated against the gold standard data determined by consensus of three experienced prosthodontists, and their accuracy was analyzed. The decision tree classifier indicated the highest accuracy at 0.8413 (Model-A) and 0.7523 (Model-B). Accuracy with the gradient boosting classifier and the random forest classifier was 0.6896, 0.6687, and 0.8413, 0.7523, respectively. Overall, the decision tree classifier had the best accuracy among the three methods. The study contributes to the implementation of AI in the decision-making process of tooth prognosis in consideration of the treatment plan.

Lactic acid (LA) is short-chain fatty acid, such as butyric acid and propionic acid, that is produced as a metabolite of lactic acid bacteria, including periodontopathic bacteria. These short-chain fatty acids have positive effects on human health but can also have negative effects, such as the promotion of periodontal disease (PD), which is caused by periodontal pathogens present in the gingival sulcus. PD is characterized by apical migration of junctional epithelium, deepening of pockets, and alveolar bone loss. Thus, the junctional epithelial cells that form the bottom of the gingival sulcus are extremely important in investigating the pathophysiology of PD. The aim of this study was to investigate the effect of LA on wound healing, cell growth, cell cycle kinetics, and gene expression of cultured junctional epithelium cells. The results showed that stimulation with 10 mM LA slowed wound healing of the junctional epithelial cell layer and arrested the cell cycle in the G0/G1 (early cell cycle) phase, thereby inhibiting cell growth. However, cell destruction was not observed. LA also enhanced mRNA expression of integrin α5, interleukin (IL)-6, IL-8, intercellular adhesion molecule-1, and receptor activator of nuclear factor kappa-B ligand. The results of this study suggest that stimulation of junctional epithelial cells with high concentrations of LA could exacerbate PD, similarly to butyric acid and propionic acid.

Purpose: To elucidate the effects of butyric acid (BA), a metabolite of bacteria involved in periodontitis, and a possible enhancer of the junctional epithelial cells. Methods: A murine junctional epithelial cell line, JE-1, was used to assess the effects of sodium butyrate (NaB) as BA. Cell proliferation, migration and attachment were analyzed. Additionally, gene and promoter expression analysis was performed, i.e., cap analysis of gene expression (CAGE) and gene ontology (GO) term enrichment analysis. Results: NaB affected junctional epithelial cell proliferation, migration and attachment. A high concentration of NaB caused cell death and a low concentration tended to promote migration and adhesion. CAGE analysis revealed 75 upregulated and 96 downregulated genes in the cells after 0.2 mM NaB stimulation for 3 h. Regarding GO term enrichment, the genes upregulated >4-fold participated predominantly in cell migration and proliferation. The results of this study suggest that BA produced from periodontopathic bacteria is involved in periodontal tissue destruction at high concentrations. Furthermore, at low concentrations, BA potentially participates in periodontal disease progression by increasing proliferation, migration and attachment of the junctional epithelium and thereby increasing epithelial down-growth.

The decline in oral health commonly occurs as a natural consequence of aging or due to various pathological factors. Tooth loss, which diminishes masticatory ability, has been associated with negative impacts on cognitive function. This observational study analyzed dental and medical records from Japan’s Universal Healthcare System (UHCS) national database to investigate the relationship between cognitive and oral disorders, focusing on periodontitis and decreased tooth-to-tooth contact between the maxillary and mandibular arches. A descriptive data analysis evaluated diagnostic codes for Alzheimer’s disease and cognitive impairment alongside dental treatment records from 2013 to 2018. The odds ratios for cognitive impairment in patients with partial loss of natural tooth contact were 1.6663 (p < 0.05) for early elderly individuals (aged 65–75) and 1.5003 (p < 0.0001) for advanced elderly individuals (over 75). Periodontally compromised patients had higher odds, with ratios of 1.3936 (p < 0.0001) for early elderly individuals and 1.1888 (p < 0.00001) for advanced elderly individuals, compared to their periodontally healthy counterparts. These findings suggest a potential link between cognitive health, natural tooth contact preservation, and periodontitis, with the loss of natural tooth contacts having the most significant impact on cognitive function.