Resumen:
Introduction: Periodontitis and apical periodontitis are multifactorial inflammatory diseases
that result in bone resorption. In periodontitis, there is the destruction of the supporting tissues
of the teeth. Environmental, behavioral, and especially genetic factors can influence its
susceptibility and progression. Among the genes studied, RANK has stood out for its role in
regulating bone remodeling. In apical periodontitis, there is resorption of periapical bone tissue,
associated with infection and treated through endodontic therapy. Due to its frequently
asymptomatic nature, screening by radiographs is essential, together with the analysis of the
epidemiological profile of the population. In this scenario, artificial intelligence stands out as a
promising tool in aiding diagnosis. Objective: To investigate genetic factors associated with
periodontitis and epidemiological factors related to apical periodontitis and endodontic
treatments in the same population. Material and Methods: The study included 527 adult
individuals living in the Recôncavo Baiano, Bahia State, Brazil. Genotyping of rs79882996 was
performed on 323 blood samples, using the GoTaq® Probe qPCR Master Mix kit (Promega)
and TaqMan® SNP Genotyping Assays (Thermo Scientific™) probe, in a QuantStudio™ 12K
Flex equipment (Applied Biosystems). Association analysis was conducted using the Plink 1.9
software, using multivariate logistic regression, with adjustment for age. Panoramic
radiographs of all participants were analyzed to identify periapical lesions, endodontic
treatment, or tooth absence, using the consensus of two experts and the AI software (DIO
platform). A descriptive analysis of the population was performed and the diagnostic accuracy
of the AI was evaluated using the parameters of sensitivity, specificity, positive predictive
value, negative predictive value, accuracy, Receiver Operating Characteristic curves, and area
under the curve. Results: rs79882996, located in the RANK gene, showed a significant
association with periodontitis. In the additive model, each additional T allele was associated
with a 64% increase in the chance of occurrence of periodontitis, while in the dominant model
the presence of at least one T allele was associated with a 69% increase in the chance of
developing the disease. The prevalence of periapical lesions was 30.85% and of endodontic
treatments, 16.13%. The presence of these lesions was associated with worse oral health
conditions. On the other hand, endodontic treatment was significantly related to higher
education, higher income, previous guidance on oral hygiene, and flossing. In the diagnosis of
periapical lesions, the area under the curve was 0.661 (95% CI = 0.606 – 0715), indicating low
diagnostic accuracy on the part of AI; The sensitivity was 31.4%, the specificity 90.1%, the
positive predictive value was 58.54%, while the negative predictive value reached 74.69%. AI
showed high agreement with specialists in the identification of teeth with endodontic treatment
(85.8%) and moderate agreement in the detection of periapical lesions (58.7%). Conclusion:
This study reinforced the role of the RANK gene in periodontitis and highlighted its potential
as a target for future therapies. In addition, this study showed the vulnerability of a population
with limited access to dental treatment. AI has shown potential in tracking population oral
health, but it still requires improvement, especially in the identification of periapical lesions.