METHODS: A ball phantom was scanned using panoramic mode of the Planmeca ProMax 3D Mid CBCT unit (Planmeca, Helsinki, Finland) with standard exposure settings used in clinical practice (60 kV, 2 mA, and maximum FOV). An automated calculator algorithm was developed in MATLAB platform. Two parameters associated with panoramic image distortion such as balls diameter and distance between middle and tenth balls were measured. These automated measurements were compared with manual measurement using the Planmeca Romexis and ImageJ software.
RESULTS: The findings showed smaller deviation in distance difference measurements by proposed automated calculator (ranged 3.83 mm) as compared to manual measurements (ranged 5.00 for Romexis and 5.12 mm for ImageJ software). There was a significant difference (p
MATERIALS AND METHODS: A total of 2306 subjects were selected from the patient archives of a large dental hospital and the chronological age for each subject was recorded. This age was assigned to each specific stage of dental development for each tooth to create a RDS. To validate this RDS, a further 484 subjects were randomly chosen from the patient archives and their dental age was assessed based on the scores from the RDS. Dental age was estimated using meta-analysis command corresponding to random effects statistical model. Chronological age (CA) and Dental Age (DA) were compared using the paired t-test.
RESULTS: The overall difference between the chronological and dental age (CA-DA) was 0.05 years (2.6 weeks) for males and 0.03 years (1.6 weeks) for females. The paired t-test indicated that there was no statistically significant difference between the chronological and dental age (p > 0.05).
CONCLUSION: The validated southern Chinese reference dataset based on dental maturation accurately estimated the chronological age.
METHODS: A random sample of digital panoramic radiographs from the database of a dental hospital was evaluated. Two calibrated examiners (κ ≥ 0.89) assessed the technical quality of the root fillings and the radiographic periapical health status by using the periapical index. Descriptive statistical analysis was carried out, followed by multilevel modeling by using tooth-level and patient-level predictors. Model fit information was obtained, and the findings of the best-fit model were reported.
RESULTS: A total of 6409 teeth were included in the analysis. The predicted probability of a tooth having AP was 0.42%. There was a statistically significant variability between patients (P