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.

Orthodontic Treatment: Real Risk for Dental Age Estimation in Adults?

Dental age estimation becomes a challenge once the root formation is concluded. In living adults, one dental age indicator is the formation of secondary dentine, also associated with orthodontic treatment as well as root shortening. The aim of this study was to establish whether these secondary effects of orthodontic treatment could generate a statistically significant difference in dental age estimations when using Kvaal's method. The study sample included 34 pairs of pre- and postorthodontic panoramic radiographs, from different individuals with exactly the same age and sex distribution. Females 65%, median age 17.5 years, and males 35%, median age 22.5 years, were included. After data collection, dental age was estimated per tooth using formulae previously published. The risk of obtaining over-estimation of age was calculated. (RR = 1.007). The changes caused by orthodontic treatment do not have any significant effect on age estimation when Kvaal et al.'s method is applied on panoramic radiographs.

Application of the Kvaal method for adult dental age estimation using Cone Beam Computed Tomography (CBCT).

Different non-invasive methods have been proposed for dental age estimation in adults, with the Kvaal et al. method as one of the more frequently tested in different populations. The purpose of this study was to apply the Kvaal et al. method for dental age estimation on modern volumetric data from 3D digital systems. To this end, 101 CBCT images from a Malaysian population were used. Fifty-five per cent were female (n = 55), and forty-five percent were male (n = 46), with a median age of 31 years for both sexes. As tomographs allow the observer to obtain a sagittal and coronal view of the teeth, the Kvaal pulp/root width measurements and ratios were calculated in the bucco-lingual and mesio-distal aspects of the tooth. From these data different linear regression models and formulae were built. The most accurate models for estimating age were obtained from a diverse combination of measurements (SEE ±10.58 years), and for the mesio-distal measurements of the central incisor at level A (SEE ±12.84 years). This accuracy, however is outside an acceptable range in for forensic application (SEE ±10.00 years), and is also more time consuming than the original approach based on dental radiographs.