Institute of Clinical Dentistry, Faculty of Dentistry, University of Oslo, Postboks 1109, Blindern, N-00317, Oslo, Norway.
Department of Forensic Sciences, Oslo University Hospital, Postboks 4950 Nydalen, OUS, Rikshospitalet, 0424, Oslo, Norway.
Int J Legal Med. 2023 Sep;137(5):1515-1526. doi: 10.1007/s00414-023-03055-5. Epub 2023 Jul 4.
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.
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.
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 for males) and 2nd molar (p=9.44×10 for males and p=7.4×10 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.
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.
通过对整个第一和第二磨牙的 MRI 分割，研究使用牙组织体积来预测亚成年人年龄超过 18 岁，并建立一种结合来自两个不同磨牙的信息的模型。
我们使用 1.5-T 扫描仪获取了 99 名志愿者的 T2 加权 MRI。使用 SliceOmatic（Tomovision©）进行分割。线性回归用于分析组织体积、年龄和性别之间的数学转换结果之间的关联。基于所选模型，根据年龄变量、共同或单独的 p 值，评估不同结果和牙齿组合的性能。通过使用来自第一和第二磨牙的信息的贝叶斯方法，分别和联合，获得年龄超过 18 岁的预测概率。
纳入了 87 名参与者的第一磨牙和 93 名参与者的第二磨牙。年龄范围为 14-24 岁，中位数年龄为 18 岁。对于右下第一（男性 p=7.1*10）和第二磨牙（男性 p=9.44×10，女性 p=7.4×10），转换结果（高信号软组织+低信号软组织）/总与年龄的统计关联最强。与单独使用最佳牙齿相比，在男性中结合右下第一和第二磨牙并不能提高预测性能。
右下第一和第二磨牙的 MRI 分割可能有助于预测亚成年人年龄超过 18 岁。我们提供了一个统计框架来结合来自两个磨牙的信息。
