Artificially intelligent scoring and classification engine for forensic identification

Abstract

Despite advances in genotyping technologies, traditional kinship analysis tools utilized in forensic identification have seen limited evolution and lack measures of accuracy. Here, we leverage artificial intelligence (AI) and extend the Elston-Stewart algorithm to deliver a method that provides an unprecedented level of flexibility to matching individuals with pedigrees by likelihood ratio. We designed an AI that utilizes 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. We demonstrate that a properly trained AI significantly and reproducibly outperforms a human interpreter. We discuss published limitations of existing tools and demonstrate that they are not amenable to the size and complexity of this 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.