Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology; National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, China.
Center of Digital Dentistry, Peking University School and Hospital of Stomatology & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, China.
Dentomaxillofac Radiol. 2023 Oct;52(7):20230177. doi: 10.1259/dmfr.20230177. Epub 2023 Jun 22.
To train a U-Net model to segment the intact pulp cavity of first molars and establish a reliable mathematical model for age estimation.
We trained a U-Net model by 20 sets of cone-beam CT images and this model was able to segment the intact pulp cavity of first molars. Utilizing this model, 239 maxillary first molars and 234 mandibular first molars from 142 males and 135 females aged 15-69 years old were segmented and the intact pulp cavity volumes were calculated, followed by logarithmic regression analysis to establish the mathematical model with age as the dependent variable and pulp cavity volume as the independent variable. Another 256 first molars were collected to estimate ages with the established model. Mean absolute error and root mean square error between the actual and the estimated ages were used to assess the precision and accuracy of the model.
The dice similarity coefficient of the U-Net model was 95.6%. The established age estimation model was [Formula: see text] ( is the intact pulp cavity volume of the first molars). The coefficient of determination (R), mean absolute error and root mean square error were 0.662, 6.72 years, and 8.26 years, respectively.
The trained U-Net model can accurately segment pulp cavity of the first molars from three-dimensional cone-beam CT images. The segmented pulp cavity volumes could be used to estimate the human ages with reasonable precision and accuracy.
训练 U-Net 模型以分割第一磨牙完整牙髓腔，并建立可靠的年龄估计数学模型。
我们用 20 组锥形束 CT 图像来训练 U-Net 模型，该模型可以分割第一磨牙完整的牙髓腔。利用该模型，对 142 名男性和 135 名女性年龄在 15-69 岁的 239 颗上颌第一磨牙和 234 颗下颌第一磨牙进行了分割，并计算了完整牙髓腔的体积，然后进行对数回归分析，建立了以年龄为因变量、牙髓腔体积为自变量的数学模型。另外收集了 256 颗第一磨牙，用建立的模型进行年龄估计。用实际年龄和估计年龄之间的平均绝对误差和均方根误差来评估模型的精度和准确性。
U-Net 模型的骰子相似系数为 95.6%。建立的年龄估计模型为[公式：见文本]（是第一磨牙的完整牙髓腔体积）。决定系数（R）、平均绝对误差和均方根误差分别为 0.662、6.72 岁和 8.26 岁。
训练有素的 U-Net 模型可以准确地从三维锥形束 CT 图像中分割第一磨牙的牙髓腔。分割后的牙髓腔体积可以用于估计人类的年龄，具有合理的精度和准确性。
