PDF(Pubmed)
Abstract：
OBJECTIVE: 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.
METHODS: 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.
CONCLUSIONS: 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.
METHODS: 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.
CONCLUSIONS: 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.
摘要：
目的：我们旨在从1日开始建立结合MRI体积测量的模型，第2磨牙和第3磨牙用于亚成人的年龄预测，并比较所有三个磨牙的不同组合的年龄预测性能，在研究队列的内部。
方法：我们使用具有定制高分辨率单T2序列的1.5TMR扫描仪检查了99名志愿者。使用SliceOmatic(Tomovision©)进行分割。年龄预测基于牙齿组织比率(高信号软组织+低信号软组织)/总数。该模型包括三个相关参数，以说明磨牙之间的统计依赖性。使用四分位距（IQR）评估了三个磨牙的不同牙齿组合的年龄预测性能。
结果：我们包括来自87名参与者的第一磨牙的数据（F/M59/28），第二磨牙93（F/M60/33）和第三磨牙67（F/M45/22）。年龄范围为14-24岁，中位年龄为18岁。具有最佳年龄预测性能（最小IQR）的模型在男性中为46-47-18（右下第一和第二和右上第三磨牙）。不同磨牙之间的估计相关性为0.620（46与47),0.430（46vs.18),和0.598（47vs.18).IQR在包括第三磨牙的牙齿组合中是最小的。
结论：我们已经建立了一个模型，用于从1日开始组合组织体积测量值，第2磨牙和第3磨牙用于亚成人年龄预测。预测性能主要由第三磨牙驱动。涉及第三摩尔的所有组合表现良好。
方法：我们使用具有定制高分辨率单T2序列的1.5TMR扫描仪检查了99名志愿者。使用SliceOmatic(Tomovision©)进行分割。年龄预测基于牙齿组织比率(高信号软组织+低信号软组织)/总数。该模型包括三个相关参数，以说明磨牙之间的统计依赖性。使用四分位距（IQR）评估了三个磨牙的不同牙齿组合的年龄预测性能。
结果：我们包括来自87名参与者的第一磨牙的数据（F/M59/28），第二磨牙93（F/M60/33）和第三磨牙67（F/M45/22）。年龄范围为14-24岁，中位年龄为18岁。具有最佳年龄预测性能（最小IQR）的模型在男性中为46-47-18（右下第一和第二和右上第三磨牙）。不同磨牙之间的估计相关性为0.620（46与47),0.430（46vs.18),和0.598（47vs.18).IQR在包括第三磨牙的牙齿组合中是最小的。
结论：我们已经建立了一个模型，用于从1日开始组合组织体积测量值，第2磨牙和第3磨牙用于亚成人年龄预测。预测性能主要由第三磨牙驱动。涉及第三摩尔的所有组合表现良好。
