Age Estimation Using Pulp/Tooth Volume Ratio of canine teeth in Cone-Beam Computed Tomography (CBCT) Images
Bibliographic record
Abstract
Background and Aim: Age estimation is an important issue in forensic medicine.Teeth are more resistant to changes than bones, so they are one of the main indicators for age estimation in forensic medicine.To determine the relationship between age and dental changes, the ratio of dental pulp/Tooth volume (PV/ TV) is one of the most appropriate indicators.The aim of this study was to determine the correlation between PV/TV ratio and chronological age in canine teeth using CBCT images. Materials and Methods:In this descriptive-analytical study, a total of 183 CBCT images were selected randomly from archive of images in an oral and maxillofacial radiology center.The images were imported into the 3D Slicer software as DICOM data.Pulp volume, tooth volume and their ratio were calculated.Data were introduced into SPSS 17.We used T-test, ANOVA, Pearson correlation coefficient and linear regression for data analysis. Results:The mean value of PV/TV ratio was not different significantly in relation to gender, teeth location (mandible and maxilla, right and left sides), but there was a significant difference in terms of age.Pearson correlation showed a significant negative relation between the age of the subjects and PV/TV ratio (R = -0.714).The results of regression analysis for all data and also for all of 6 age groups showed significant correlation only in the age groups of 15-19 and 50-59 years old.Conclusion: The results of this study showed that there was an inverse and significant relationship between PV/TV ratio and chronological age.Also, in the age groups of 15-19 and 50-59 years, it seems that regression model can be considered a reliable method to estimate age in Iranian population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".