Login / Signup

Machine learning assisted 5-part tooth segmentation method for CBCT-based dental age estimation in adults.

R Merdietio BoediS ShepherdF OscandarA J FrancoS Mânica
Published in: The Journal of forensic odonto-stomatology (2024)
No multicollinearity was detected in the models. The best performing model comes from maxillary lateral incisor using SVR with polynomial kernel ( = 0.73). The lowest error rate achieved by the model was given also by maxillary lateral incisor, with 4.86 years of mean average error and 6.05 years of root means squared error. However, demands a complex approach to segment the enamel volume in the crown section and a lengthier labour time of 45 minutes per tooth.
Keyphrases
  • machine learning
  • cone beam computed tomography
  • minimally invasive
  • deep learning
  • convolutional neural network
  • magnetic resonance
  • magnetic resonance imaging
  • oral health
  • image quality