MRI segmentation of tooth tissue in age prediction of sub-adults - a new method for combining data from the 1st, 2nd, and 3rd molars.

PURPOSE: We aimed to establish a model combining MRI volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults and compare the age prediction performance of different combinations of all three molars, internally in the study cohort. MATERIAL AND METHOD: We examined 99 volunteers using a 1.5 T MR scanner with a customized high-resolution single T2 sequence. Segmentation was performed using SliceOmatic (Tomovision©). Age prediction was based on the tooth tissue ratio (high signal soft tissue + low signal soft tissue)/total. The model included three correlation parameters to account for statistical dependence between the molars. Age prediction performance of different combinations of teeth for the three molars was assessed using interquartile range (IQR). RESULTS: We included data from the 1st molars from 87 participants (F/M 59/28), 2nd molars from 93 (F/M 60/33) and 3rd molars from 67 (F/M 45/22). The age range was 14-24 years with a median age of 18 years. The model with the best age prediction performance (smallest IQR) was 46-47-18 (lower right 1st and 2nd and upper right 3rd molar) in males. The estimated correlation between the different molars was 0.620 (46 vs. 47), 0.430 (46 vs. 18), and 0.598 (47 vs. 18). IQR was the smallest in tooth combinations including a 3rd molar. CONCLUSION: We have established a model for combining tissue volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults. The prediction performance was mostly driven by the 3rd molars. All combinations involving the 3rd molar performed well.

Prediction of Age Older than 18 Years in Sub-adults by MRI Segmentation of 1st and 2nd Molars.

PURPOSE: To investigate prediction of age older than 18 years in sub-adults using tooth tissue volumes from MRI segmentation of the entire 1st and 2nd molars, and to establish a model for combining information from two different molars. MATERIALS AND METHODS: We acquired T2 weighted MRIs of 99 volunteers with a 1.5-T scanner. Segmentation was performed using SliceOmatic (Tomovision©). Linear regression was used to analyse the association between mathematical transformation outcomes of tissue volumes, age, and sex. Performance of different outcomes and tooth combinations were assessed based on the p-value of the age variable, common, or separate for each sex, depending on the selected model. The predictive probability of being older than 18 years was obtained by a Bayesian approach using information from the 1st and 2nd molars both separately and combined. RESULTS: 1st molars from 87 participants, and 2nd molars from 93 participants were included. The age range was 14-24 years with a median age of 18 years. The transformation outcome (high signal soft tissue + low signal soft tissue)/total had the strongest statistical association with age for the lower right 1st (p= 7.1*10-4 for males) and 2nd molar (p=9.44×10-7 for males and p=7.4×10-10 for females). Combining the lower right 1st and 2nd molar in males did not increase the prediction performance compared to using the best tooth alone. CONCLUSION: MRI segmentation of the lower right 1st and 2nd molar might prove useful in the prediction of age older than 18 years in sub-adults. We provided a statistical framework to combine the information from two molars.

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

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. Segmentation 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, combined 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.

Digitalised exercise material in forensic odontology.

INTRODUCTION: This paper presents digital educational material in forensic odontology, including dental identification after multiple fatalities and dental age estimation from different age groups. MATERIAL AND METHOD: Electronic patient records consisting of intraoral scans of the dentition, digital radiographs, photographs and written dental records were collected. Exercises in age estimations contained digital radiographs and photographs of ground tooth sections, with digital measuring tools and tables according to age groups. The teaching material was organised as a module in an electronic Learning Management System with external links to all relevant teaching material. RESULTS: For the identification exercises, intraoral scans and the latest digital radiographs simulated the postmortem examination of the deceased. For comparison, all other radiographs, photographs and dental records were available as antemortem material. The exercise was to match postmortem findings with the antemortem records using the Interpol standard and reconciliation. Age assessment of children used designated tables to grade tooth development on digital radiographs. For adults, non-destructive methods, digital radiographs, photographs and measuring tools were used. DISCUSSION: The teaching concept was hybrid, but it can easily be adapted as a fully digital exercise. The instructions and written material can be translated into different languages. The level of difficulty in the exercises can be adjusted according to the participant's level of knowledge. CONCLUSION: The educational material embraces the new possibilities for digitalisation and intraoral scanning. This might be a valuable tool for motivating and engaging the students in their participation and understanding of the subject.