Login / Signup

Detection of the internal anatomy of lower anterior teeth using cone-beam computed tomography.

Marice C SantosAline Evangelista de Souza GabrielAntonio Miranda da Cruz FilhoManoel Damião de Sousa-NetoRicardo Gariba Silva
Published in: Australian endodontic journal : the journal of the Australian Society of Endodontology Inc (2021)
Morphology study of root canal systems is essential for a correct diagnosis, therapy and prognosis of root canal treatment. This study aimed to analyse the dental anatomy of the lower anterior teeth, using cone-beam computed tomography (CBCT). Lower anterior teeth were classified in terms of type, number and location of root canals, evaluating the bilaterality of anatomical occurrences and determining whether the gender and age influence the findings. We analysed 749 CBCT of patients attending the School of Dentistry for different reasons. Spearman's correlations and Wilcoxon signed-rank test were used to analyse data (α = 0.05). There was no significant correlation between gender (male and female) and anatomy of the canals 33 (P = 0.162), 32 (P = 0.815), 31 (P = 0.708), 41 (P = 0.422), 42 (P = 0.382) and 43 (P = 0.063). There was a significant correlation between age and anatomy of the canals 33 (P = 0.045), 32 (P = 0.033), 31 (P = 0.022), 41 (P = 0.000), 42 (P = 0.037) and 43 (P = 0.037). There was no significant correlation between gender and patients' age (P = 0.325). There was no anatomical difference between the bilateral pairs (right and left homologous teeth) (P > 0.05). The most common anatomical configuration was single-canal teeth (85.29%), followed by the configuration in which one canal leaves the chamber, divides into two and unite again (12.88%). Anatomy of the lateral incisors and lower canines does not change with the gender of patients. However, as age rises, single canals and the incidence of division into two canals ending in a single foramen also increases.
Keyphrases
  • cone beam computed tomography
  • newly diagnosed
  • ejection fraction
  • mental health
  • physical activity
  • stem cells
  • machine learning
  • patient reported outcomes
  • big data
  • sensitive detection
  • cell therapy