3D segmentation of dental crown for volumetric age estimation with CBCT imaging.
Rizky Merdietio BoediSimon ShepherdFahmi OscandarScheila MânicaAdemir FrancoPublished in: International journal of legal medicine (2022)
In adult dental age estimation, segmentation of dental volumetric information from different tooth parts using cone-beam computed tomography (CBCT) has proven beneficial in improving the regression model reliability. This segmentation method can be expanded in the crown part since the volumetric information in the crown is affected by attrition in the enamel and secondary dentine in the dentine and pulp chamber. CBCT scans from 99 patients aged between 20 and 60 were collected retrospectively. A total of 80 eligible teeth for each tooth type were used in this study. The enamel to dentine volume ratio (EDVR), pulp to dentine volume ratio (PDVR) and sex were used as independent variables to predict chronological age (CA). The EDVR was not affected by PDVR. The highest R 2 was calculated from the maxillary canine (R 2 = 0.6). The current approach in crown segmentation has proven to improve model performance in anterior maxillary teeth.
Keyphrases
- cone beam computed tomography
- deep learning
- convolutional neural network
- oral health
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- computed tomography
- high resolution
- healthcare
- magnetic resonance imaging
- patient reported outcomes
- machine learning
- patient reported
- fluorescence imaging
- childhood cancer