Since despite advances in genotyping technologies, traditional kinship analysis tools used in forensic identification have seen limited evolution and lack measures of accuracy, the current study leveraged artificial intelligence (AI) and extended the Elston-Stewart algorithm to deliver a method that provides an unprecedented level of flexibility to matching individuals with pedigrees by likelihood ratio.
Researchers designed an AI that uses a prediction cascade based on gradient descent logistic regression which allows for iterative solution of multi missing person scenarios. Furthermore, the AI can quantify the confidence underlying likelihood ratios across the spectrum of pedigrees, regardless of the amount of genetic information available and the number of missing persons. The algorithm accommodates an arbitrary number of generations and ancestral relationships, including multiple marriages, mutations, and consanguinity. This project demonstrated that a properly trained AI significantly and reproducibly outperforms a human interpreter. This article discusses published limitations of existing tools and demonstrates that they are not amenable to the size and complexity of the current study. This novel method significantly improves the trade-off between sensitivity and specificity beyond the limits of traditional kinship analysis tools and introduces opportunities beyond the field of forensic genetics. (publisher abstract modified)