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Assessment of dental age estimation using dentinal translucency in ground sections of single rooted teeth: a digital image analysis.

Abelene Maria DurandMadhu NarayanRaghavendhar KarthikRajkumar KrishnanNarashimhan SrinivasanDineshkumar Thayalan
Published in: Anatomy & cell biology (2024)
Human dentition is unique to individuals and helps in identification of individuals in forensic odontology. This study proposes to study the manually ground sections of single rooted teeth using digital methods for dental age estimation. To assess the dentinal translucency from the scanned digital images of manually ground section of teeth using commercially available image edition software. Corroborating the root dentinal translucency length and region of interest (ROI) of translucency zone in pixels (as a marker of dental age) with the chronological age of the subject, as stratified by different age groups. Twenty single-rooted extracted teeth from 20 patients each from 6 groups divided as per age. Manual sectioning of the teeth followed by scanning the sections was done. Root area in pixels and ROI of translucency zone were measured. From the observed values, translucency length percentage (TLP) and percentage of ROI in pixels (TPP) was calculated and tabulated. Pearson's correlation coefficients were obtained for age with TLP and TPP. Positive correlation existed between age and TLP and also between age and TPP. With the obtained data, multilinear regression equations for specific age groups based on 10-year intervals were derived. By a step-down analysis method, age was estimated with an average error of around ±7.9 years. This study gives a novel method for age-estimation that can be applied in real-time forensic sciences.
Keyphrases
  • deep learning
  • oral health
  • mass spectrometry
  • ejection fraction
  • newly diagnosed
  • convolutional neural network
  • patient reported outcomes