A machine learning approach for age prediction based on trigeminal landmarks.

Objective: The aim of this study was to estimate the chronological age (CA) of a growing individual using a new machine learning approach on Cone Beam Computed Tomography (CBCT).

Materials And Methods: The dataset included 48 CBCT and hand-wrist radiographs of growing individuals. 12 landmarks related to trigeminal trajectories were plotted on each CBCT and principal component analysis was applied for dimensionality reduction.