Regressive changes of crown‐root morphology and their volumetric segmentation for adult dental age estimation

Cone‐beam computed tomography (CBCT) enables the assessment of regressive morphological changes in teeth, which can be used to predict chronological age (CA) in adults. As each tooth region is known to have different correlations with CA, this study aimed to segment and quantify the sectional volume...

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Bibliographic Details
Published in:Journal of forensic sciences 2022-09, Vol.67 (5), p.1890-1898
Main Authors: Merdietio Boedi, Rizky, Shepherd, Simon, Oscandar, Fahmi, Mânica, Scheila, Franco, Ademir
Format: Article
Language:English
Subjects:
Age
Chronology
Computed tomography
cone‐beam computed tomography
dental age estimation
forensic dentistry
Morphology
Regression models
root resorption
secondary dentin
Segmentation
Teeth
tooth attrition
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Summary:Cone‐beam computed tomography (CBCT) enables the assessment of regressive morphological changes in teeth, which can be used to predict chronological age (CA) in adults. As each tooth region is known to have different correlations with CA, this study aimed to segment and quantify the sectional volumes of the tooth crown and root from CBCT scans to test their correlations with the chronological age (CA). Seventy‐five CBCT scans from individuals with age between 20 and 60 years were collected retrospectively from an existing database. A total of 192 intact maxillary anterior teeth fulfilled the eligibility criteria. The upper tooth volume ratio (UTVR), lower tooth volume ratio (LTVR), and sex were used as predictor variables. The UTVR and LTVR parameters were both found to be differently correlated to CA and independent from each other. Regression models were derived from each tooth, with the highest R2 being the maxillary lateral incisor (R2 = 0.67). Additional single predictor models using each ratio were capable of reliably predicting the CA. The segmentation approach in volumetric adult dental age estimation proved to be beneficial in enhancing the reliability of the regression model.
ISSN:0022-1198
1556-4029
DOI:10.1111/1556-4029.15094