Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach

TL;DR

This paper investigates automating human decomposition stage classification using deep learning models trained on large datasets, demonstrating AI's potential to match human expert reliability in forensic analysis.

Contribution

It introduces a novel application of deep learning models, Inception V3 and Xception, for automating SOD classification across multiple anatomical regions in forensic images.

Findings

Xception achieved macro F1 scores above 0.87 for most regions.

AI models performed comparably to human experts in reliability.

Deep learning can effectively automate SOD identification in forensic contexts.

Abstract

Determining the stage of decomposition (SOD) is crucial for estimating the postmortem interval and identifying human remains. Currently, labor-intensive manual scoring methods are used for this purpose, but they are subjective and do not scale for the emerging large-scale archival collections of human decomposition photos. This study explores the feasibility of automating two common human decomposition scoring methods proposed by Megyesi and Gelderman using artificial intelligence (AI). We evaluated two popular deep learning models, Inception V3 and Xception, by training them on a large dataset of human decomposition images to classify the SOD for different anatomical regions, including the head, torso, and limbs. Additionally, an interrater study was conducted to assess the reliability of the AI models compared to human forensic examiners for SOD identification. The Xception model achieved the best classification performance, with macro-averaged F1 scores of .878, .881, and .702 for the head, torso, and limbs when predicting Megyesi's SODs, and .872, .875, and .76 for the head, torso, and limbs when predicting Gelderman's SODs. The interrater study results supported AI's ability to determine the SOD at a reliability level comparable to a human expert. This work demonstrates the potential of AI models trained on a large dataset of human decomposition images to automate SOD identification.