Generation and validation of a new linear regression equation of mandibular first permanent molar and its comparative evaluation for predictive accuracy with already established regression equation of cervical vertebrae: a radiographic study

Chetan Palaskar; Ananya Hazare; Usha Shenoy; Himija Karia; Pritam Khorgade; Nivedita Nandeshwar

doi:10.18203/2319-2003.ijbcp20261832

Authors

Chetan Palaskar VSPM'S Ranjeet Deshmukh Dental College and Research Centre, Nagpur, Maharashtra, India
Ananya Hazare VSPM'S Ranjeet Deshmukh Dental College and Research Centre, Nagpur, Maharashtra, India
Usha Shenoy VSPM'S Ranjeet Deshmukh Dental College and Research Centre, Nagpur, Maharashtra, India
Himija Karia VSPM'S Ranjeet Deshmukh Dental College and Research Centre, Nagpur, Maharashtra, India
Pritam Khorgade VSPM'S Ranjeet Deshmukh Dental College and Research Centre, Nagpur, Maharashtra, India
Nivedita Nandeshwar VSPM'S Ranjeet Deshmukh Dental College and Research Centre, Nagpur, Maharashtra, India

DOI:

https://doi.org/10.18203/2319-2003.ijbcp20261832

Keywords:

Age estimation, Mandibular first molar, Pulp chamber height, Cervical vertebral maturation, Forensic dentistry, Orthodontics

Abstract

Background: Accurate skeletal age estimation is essential for orthodontic treatment planning and forensic identification. This study aimed to generate a new linear regression equation for age estimation using the pulp chamber crown trunk height ratio (PCTHR) of the permanent mandibular first molar and to compare its accuracy with the established cervical vertebral maturation index (CVMI) regression equation.

Methods: This observational analytical cohort study included 305 subjects aged 9–14 years. Orthopantomograms (OPGs) and lateral cephalograms were obtained. Pulp chamber height (PCH) and crown root trunk height (CRTH) of the mandibular first molar was measured using Image J 1.46r software, and PCTHR was calculated. Cervical vertebral bone age was calculated using the Mito et al regression equation: Cervical vertebral bone age (years)=−0.20 +6.20×(AH3/AP3) + 5.90×(AH4/AP4) + 4.74×(AH4/PH4). A linear regression equation for age estimation using PCTHR was derived from 305 subjects and validated on a subset of 90 subjects (15 per age group). Accuracy was compared using correlation coefficient (R), R², mean absolute error (MAE), and root mean square error (RMSE). Statistical analysis was performed using statistical package for the social sciences (SPSS) version 25.0 (IBM Corp., Armonk, NY, USA).

Results: The mean age of the 305 subjects was 12.01±1.634 years. PCH showed a weak negative correlation with age (r=–0.110). The derived regression equation was: predicted age (9–14 years) =11.362–(0.456×PCH) + (0.222×CRTH). The model explained 75% of the variance (R²=0.750, R=0.866) with MAE 0.719 years and RMSE 0.854 years. The CVMI model showed stronger correlation (R=0.895, R²=0.800) with lower error (MAE 0.599 years, RMSE 0.763 years).

Conclusions: Both PCTHR and CVMI methods are effective for skeletal age estimation in Indian children aged 9–14 years. CVMI demonstrated superior accuracy, but the PCTHR method offers a useful non-invasive alternative using routine OPGs without additional radiation.

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