Age estimation from iliac auricular surface using Bayesian inference and principal component analysis: a CT-based...

Area of Science:

• Forensic Anthropology
• Radiology
• Human Identification

Background:

• Age estimation is crucial for human identification.
• The auricular surface of the ilium is a reliable skeletal indicator for aging, especially in older adults.
• The Buckberry-Chamberlain method offers an objective, component-based approach to auricular age estimation.

Purpose of the Study:

• To evaluate the applicability of the Buckberry-Chamberlain auricular age estimation method in an Indian population.
• To assess age-related changes on the auricular surface using computed tomography (CT).
• To compare the accuracy of individual morphological features and develop a summary age model.

Main Methods:

• CT scans of 435 Indian participants were analyzed for age-related auricular changes.
• Three Buckberry-Chamberlain morphological features (macroporosity, transverse organization, apical changes) were assessed.
• Bayesian inference and transition analysis were used for age estimation from individual features and for developing multivariate summary age models.

Main Results:

• Macroporosity showed the highest accuracy (98.64%) with an error rate of 12.99 years.
• Transverse organization and apical changes yielded accuracies of 91.67% and 94.84%, respectively.
• A summary age model integrating all features reduced the inaccuracy to 8.52 years.

Conclusions:

• The Buckberry-Chamberlain method, particularly macroporosity, is effective for age estimation in the Indian population via CT scans.
• Bayesian analysis provides reliable age estimates from individual features.
• Multivariate summary age models offer improved accuracy and reliability for age estimation.