#11637. A pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles

August 2026publication date
Proposal available till 05-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Law;
Pathology and Forensic Medicine;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. In the present study, AI-assisted diagnosis was shown to have correct prediction about 70% of the time. In pedestrians struck by trucks, more severe injuries were appreciated in the facial skeleton, lungs, major airways, liver, and spleen as well as in the sternum/clavicle/rib complex, whereas the lower extremities were more affected by fractures in pedestrians struck by cars. Although the distinction of the striking vehicle should develop beyond autopsy evidence alone, the presented approach which is novel in the realm of forensic science, is shown to be effective in building automated decision support systems. Preliminary results from the application of machine learning algorithms with real-world datasets seem to highlight the efficacy of the proposed approach, which could be used for further studies concerning this topic.
Keywords:
Artificial Intelligence-based forensics; Autopsy; Classification; Injury pattern; Supervised machine learning; Traffic collision

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