Mathematically optimal decisions in forensic age assessment.

Area of Science:

• Forensic Science
• Biostatistics
• Radiology

Background:

• Forensic age estimation relies on indicators like teeth and skeleton, which inherently have uncertainty.
• Accurate age prediction remains challenging, necessitating methods for optimal decision-making under uncertainty.

Purpose of the Study:

• To apply mathematical theory for statistically optimal decisions in forensic age assessment.
• To explore the necessity and application of individual prior probability distributions for age.

Main Methods:

• Utilized mathematical theory for statistically optimal decision-making in age assessment.
• Developed a framework assuming standardized data collection and prior probability distributions for age.
• Applied the theoretical framework to Magnetic Resonance Imaging (MRI) data of distal femur and third molar maturity.

Main Results:

• Individual prior distributions for age, selected by caseworkers, are likely necessary for optimal decisions.
• Information can be collected over time to enhance the robustness of age assessment procedures.
• A weak positive conditional correlation was observed between distal femur and third molar maturity.

Conclusions:

• Optimal forensic age estimation requires statistically sound decision-making under uncertainty.
• Individualized prior knowledge, rather than a common prior, is crucial for improving age assessment accuracy.
• The proposed theoretical framework provides a robust approach for forensic age estimation using maturity indicators.