ARTIFICIAL INTELLIGENCE – APPLICATION IN FORENSIC MEDICINE

Abstract

Introduction. Artificial neural networks are widely utilized in medical fields, such as dentistry, molecular genetics, immunology, cardiology, and others. Forensic medicine is no exception, as artificial neural networks are also beginning to find applications in this field. The aim of this study was to demonstrate the potential for predicting human anthropometric parameters using dermatoglyphic parameters, which could enhance the method of dermatoglyphic identification. Materials and methods. We analyzed dermatoglyphs of the hands and feet from 567 individuals aged 18 to 59 years, with no genetic or endocrine disorders and no musculoskeletal problems. Results and discussion. The outcome of our work resulted in the development of the "Dermatoglyphics For Prediction (DFP)" program [Author's Certificate No. 74561. Computer program "Forensic Medical Identification Program using Artificial Neural Networks" Registration date: 07.11.2017]. This software device, after appropriate training, enables the prediction of an individual's ethnic-territorial affiliation and the presence of specific anthropometric parameters using such input data as dermatoglyphs of the hands and feet. Conclusions. The increasing needs of the Ukrainian community for the identification of unknown individuals, given the geopolitical situation related to Russian invasion in Ukraine (the constant threat of ballistic missile attacks and unmanned aerial vehicles across the entire territory of Ukraine, which could lead to mass casualties), justify the relevance and the search for innovative approaches to dermatoglyphic identification expertise, utilizing state-of-the-art technologies, particularly neural network-based prediction of anthropometric parameters, sex, and ethnic-territorial affiliation of an unknown person, using input parameters such as dermatoglyphs of the hands and feet, with the aim of enhancing the evidentiary value of identification expertise. This software device, after appropriate training, allows for the prediction of ethnic-territorial affiliation and the presence of specific anthropometric parameters in the examined individual using such input data as dermatoglyphs of the hands and feet.