Objective: Detection of explosives is a challenge due to the use of improvised and concealed bombs. Post-bomb strike bodies are handled by emergency and forensic teams. We aimed to determine whether medical dual-energy computed tomography (DECT) algorithm and prediction model can readily detect and distinguish a range of explosives on the human body during disaster victim identification (DVI) processes of bombings.
Materials and methods: A medical DECT of 8 explosives (Semtex, Pastex, Hexamethylene triperoxide diamine, Acetone peroxide, Nitrocellulose, Pentrite, Ammonium Nitrate, and classified explosive) was conducted ex-vivo and on an anthropomorphic phantom. Hounsfield unit (HU), electron density (ED), effective atomic number (Zeff), and dual energy index (DEI),were compared by Wilcoxon signed rank test. Intra-class (ICC) and Pearson correlation coefficients (r) were computed. Explosives classification was performed through a prediction model with test-retest samples.
Results: Except for DEI (p = 0.036), means of HU, ED, and Zeff were not statistically different (p > 0.05) between explosives ex-vivo and on the phantom (r > 0.80). Intra- and inter-reader ICC were good to excellent: 0.806 to 0.997 and 0.890, respectively. Except for the phantom DEI, all measurements from each individual explosive differed significantly. HU, ED, Zeff, and DEI differed depending on the type of explosive. Our decision tree provided Zeff and ED for explosives classification with high accuracy (83.7%) and excellent reliability (100%).
Conclusion: Our medical DECT algorithm and prediction model can readily detect and distinguish our range of explosives on the human body. This would avoid possible endangering of DVI staff.
Keywords: Artificial intelligence; Computer-assisted image processing; Explosives; Forensic medicine; Machine learning.