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Age estimation from iliac auricular surface using Bayesian inference and principal component analysis: a CT-based study in an Indian population

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Abstract

Age estimation constitutes one of the pillars of human identification. The auricular surface of the ilium presents as a durable and robust structure within the human skeletal framework, capable of enabling accurate age estimation in older adults. Amongst different documented auricular age estimation methods, the Buckberry-Chamberlain method offers greater objectivity through its component-based approach. The present study aimed to test the applicability of the Buckberry-Chamberlain method in an Indian population through a CT-based examination of the auricular surface. CT scans of 435 participants undergoing CT examinations following the advice of their treating physicians were scrutinized for different age-related auricular changes. Three of the five morphological features described by Buckberry-Chamberlain could be appreciated on CT scans, and thus further statistical analysis was restricted to these features. Transition analysis coupled with Bayesian inference was undertaken individually for each feature to enable age estimation from individual features, while circumventing age mimicry. A Bayesian analysis of individual features yielded highest accuracy percentages (98.64%) and error rates (12.99 years) with macroporosity. Transverse organization and apical changes yielded accuracy percentages of 91.67% and 94.84%, respectively, with inaccuracy computations of 10.18 years and 11.74 years, respectively. Summary age models, i.e. multivariate age estimation models, derived by taking this differential accuracy and inaccuracy into consideration yielded a reduced inaccuracy value of 8.52 years. While Bayesian analysis undertaken within the present study enables age estimation from individual morphological features, summary age models appropriately weigh all appreciable features to yield more accurate and reliable estimates of age.

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Acknowledgements

This research article is a part of an ongoing doctoral research being conducted by the principal author in the Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, India. The principal author is grateful to the University Grants Commission, New Delhi for awarding the research fellowship (UGC-JRF) for pursuing PhD. The authors are also thankful to the individuals who participated in this study, and to the authorities of the institution for allowing us to conduct this research.

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Authors and Affiliations

  1. Department of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, 342005, India

    Varsha Warrier & Tanuj Kanchan

  2. School of Forensic Sciences, National Forensic Sciences University, Agartala, Tripura, 799001, India

    Rutwik Shedge

  3. Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Jodhpur, 342005, India

    Pawan Kumar Garg

  4. Department of Anatomy, All India Institute of Medical Sciences, Jodhpur, 342005, India

    Shilpi Gupta Dixit

  5. Department of Anthropology (UGC Centre of Advanced Study), Panjab University, Chandigarh, 160014, India

    Kewal Krishan

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  1. Varsha Warrier
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Correspondence to Varsha Warrier.

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Warrier, V., Shedge, R., Garg, P.K. et al. Age estimation from iliac auricular surface using Bayesian inference and principal component analysis: a CT-based study in an Indian population. Forensic Sci Med Pathol (2023). https://doi.org/10.1007/s12024-023-00637-y

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