Mathematically optimal decisions in forensic age assessment
Journal article, 2021

Forensic age estimation generally involves considerable amounts of uncertainty. Forensic age indicators such as teeth or skeleton images predict age only approximately, and this is likely to remain true even for future forensic age indicators. Thus, forensic age assessment should aim to make the best possible decisions under uncertainty. In this paper, we apply mathematical theory to make statistically optimal decisions to age assessment. Such an application is fairly straightforward assuming there is a standardized procedure for obtaining age indicator information from individuals, assuming we have data from the application of this procedure to a group of persons with known ages, and assuming the starting point for each individual is a probability distribution describing prior knowledge about the persons age. The main problem is then to obtain such a prior. Our analysis indicates that individual priors rather than a common prior for all persons may be necessary. We suggest that caseworkers, based on individual case information, may select a prior from a menu of priors. We show how information may then be collected over time to gradually increase the robustness of the decision procedure. We also show how replacing individual prior distributions for age with individual prior odds for being above an age limit cannot be recommended as a general method. Our theoretical framework is applied to data where the maturity of the distal femur and the third molar is observed using MRI. As part of this analysis we observe a weak positive conditional correlation between maturity of the two body parts.

Author

Petter Mostad

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Andreas Schmeling

University Hospital Münster

Fredrik Tamsen

Uppsala University

International Journal of Legal Medicine

0937-9827 (ISSN) 1437-1596 (eISSN)

Vol. In Press

Subject Categories

Pharmacology and Toxicology

Computer Vision and Robotics (Autonomous Systems)

Genetics

DOI

10.1007/s00414-021-02749-y

More information

Latest update

1/4/2022 1