A systematic review on Artificial Intelligence for Forensic sex and age estimation with diagnostic imaging tools.

Abstract

Abstract Background: The article aims to study artificial intelligence and compare the results of different AI algorithms in collaboration with radio diagnostic devices for age and sex estimation for forensic benefits. Methods: Articles published between January 2012 and April 2022 were searched using different databases. Twenty-six articles were selected based on inclusion and exclusion criteria. Prisma guidelines were followed in the synthesis of this article. Conclusions: Artificial intelligence (AI) is a technology that involves computerized algorithms to dichotomize complex data. AI is widely used in diagnostic imaging to detect and quantify a clinical condition. This systematic review aimed to explain the role of AI in the diagnostic imaging modality of radiology in forensic Identification. AI technology is now widely used for age and sex estimation. Most of the AI models are based on machine learning (ML) programs, artificial neural networks (ANN), and convolutional neural networks (CNN). The results of the studies are promising, providing great accuracy and decision-making. These AI-based models will act as identification tools in mass disaster and medicolegal cases. In cooperation with ML algorithms can increase the identification of unknown skeleton remains. Further improvement in AI programs and diagnostic tools is needed for better accuracy and specificity in Forensic investigations. Realistic applications of these models are needed, and the accuracy rate can be enhanced by comparing these models to different populations with wide sample sizes. Most of the studies in this review paper were conducted on healthy subjects, studies on subjects having developmental disorders should also be conducted for validation of these algorithms so that they can be used in any scenario.