This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. Specifically, the vehicle type is predicted on the basis of traumatic injury suffered by casualties, exploiting machine learning algorithms. 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. Outcomes from this system can provide valuable information after the execution of autoptic examinations supporting the forensic investigation. 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.

A pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles / M. Casali, D. Malchiodi, C. Spada, A.M. Zanaboni, R. Cotroneo, D. Furci, A. Sommariva, U. Genovese, A. Blandino. - In: JOURNAL OF FORENSIC AND LEGAL MEDICINE. - ISSN 1752-928X. - 84(2021 Nov), pp. 102256.1-102256.7. [10.1016/j.jflm.2021.102256]

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

M. Casali
Primo
;
D. Malchiodi
Secondo
;
A.M. Zanaboni;R. Cotroneo;D. Furci;A. Sommariva;U. Genovese
Penultimo
;
A. Blandino
Ultimo
2021

Abstract

This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. Specifically, the vehicle type is predicted on the basis of traumatic injury suffered by casualties, exploiting machine learning algorithms. 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. Outcomes from this system can provide valuable information after the execution of autoptic examinations supporting the forensic investigation. 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.
Traffic collision; Injury pattern; Autopsy; Classification; Supervised machine learning; Artificial Intelligence-based forensics
Settore MED/43 - Medicina Legale
Settore INF/01 - Informatica
9-ott-2021
https://www.sciencedirect.com/science/article/pii/S1752928X21001414
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/875382
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