AI-Driven Virtual Simulations for Understanding Insect Colonization Dynamics in Forensic Science

Abstract

Forensic entomology employs insects to estimate postmortem intervals (PMI), detect toxins, and trace corpse relocation. However, traditional methodologies are often plagued by inconsistencies, a shortage of experts, and time-consuming analyses. This chapter investigates the potential of artificial intelligence (AI)-driven pattern recognition to transform the study of insect colonization dynamics in forensic investigations. By incorporating machine learning (ML) and deep learning (DL) techniques such as convolutional neural networks (CNNs) for larval stage identification, elliptic Fourier transformations for wing outline analysis, and entomological radar for remote monitoring AI facilitates automated species classification, PMI prediction through degree-day models, and the simulation of succession patterns across various environments. Comparative analyses underscore AI’s superiority over manual morphological assessments, offering enhanced reproducibility, reduced bias, and expedited processing of extensive datasets. Case studies from Spain illustrate AI’s efficacy in resolving unsolved murders through the analysis of fragmentary evidence. Ethical challenges, including algorithmic bias and privacy concerns, are addressed alongside collaborative frameworks for integration with law enforcement. Future trends emphasize the development of expansive repositories, cross-disciplinary AI models, and virtual simulations for climate-adaptive PMI forecasting. Ultimately, AI enhances forensic entomology by improving accuracy, efficiency, and outcomes within the justice system, while bridging knowledge gaps in underrepresented regions.