Age prediction in sub-adults based on MRI segmentation of 3rd molar tissue volumes

Abstract

Purpose Our aim was to investigate tissue volumes measured by MRI segmentation of the entire 3rd molar for prediction of a sub-adult being older than 18 years. Material and method We used a 1.5-T MR scanner with a customized high-resolution single T2 sequence acquisition with 0.37 mm iso-voxels. Two dental cotton rolls drawn with water stabilized the bite and delineated teeth from oral air. Segmen- tation of the different tooth tissue volumes was performed using SliceOmatic (Tomovision © ). Linear regression was used to analyze the association between mathematical transformation outcomes of the tissue volumes, age, and sex. Performance of different transformation outcomes and tooth combinations were assessed based on the p value of the age variable, com- bined or separated for each sex depending on the selected model. The predictive probability of being older than 18 years was obtained by a Bayesian approach. Results We included 67 volunteers (F/M: 45/22), range 14–24 years, median age 18 years. The transformation outcome (pulp + predentine)/total volume for upper 3rd molars had the strongest association with age (p = 3.4 × 10 −9 ). Conclusion MRI segmentation of tooth tissue volumes might prove useful in the prediction of age older than 18 years in sub-adults.