Minimal age principle versus Bayesian approach to combine age indicators from magnetic resonance imaging for multifactorial forensic age estimation

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Multifactorial age estimation is preferred over methods based on a single anatomical site. The main challenge of the multifactorial methods lies in calculating the overall prediction interval. This study compared the performance of two approaches to achieve this: the minimal age principle versus a Bayesian approach. MRI of the third molars, left hand/wrist, and sternal extremity of both clavicles were prospectively conducted in 335 healthy Austrian Caucasian males aged 13-24 years. Development was staged according to De Tobel et al. Multi-factorial age estimation: A Bayesian approach combining dental and skeletal magnetic resonance imaging. Forensic Sci Int. 2020;306:110054. Applying the minimal age principle rendered a mean absolute error of 1.47 years, root mean square error of 1.81 years, mean width of the 95% prediction interval (PI) of 4.44 ± 2.49 years, and coverage of 68.7%. For the Bayesian approach, the results were 1.41, 1.80, 5.15 ± 1.94 years, and 81.5%, respectively. Higher inconsistency between the different age indicators was linked to a lower coverage probability in the minimal age principle, but not in the Bayesian approach. Moreover, higher inconsistency between age indicators was also linked to a higher probability of obtaining an impossible PI with the minimal age principle. Furthermore, applying the minimal age principle rendered 97.9%/81.0% correctly categorized adults (based on the point prediction of age/based on the PI) and 69.2%/85.6% correctly categorized minors. For the Bayesian approach, the results were 95.2%/76.2% and 81.5%/95.9%, respectively. In conclusion, the Bayesian approach outperformed the minimal age principle for multifactorial forensic age estimation, allowing the construction of more appropriate PIs and more correctly categorized minors.

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