Systematic review of finite element analysis utilisation in craniofacial gunshot wounds

Development and validation of risk of bias tool for the use of finite element analysis in dentistry (ROBFEAD)

There has been a systematic review of studies that used FEA in dental sciences, but no adequate risk of bias (RoB) analysis technique has been developed. Therefore, the development and validation process of RoB in studies using the finite element analysis in dentistry (ROBFEAD) tool is described. In the first phase of development, the scope of the tool and possible modifications were covered, and validation was done in the second phase. The developed tool comprised 6 domains and a total of 22 guiding questions in these domains. This article proposes the development and validation of ROBFEAD, a tool for measuring RoB in finite element research in dentistry.

The finite element method-based pattern recognition approach for the classification of patient-specific gunshot injury

Violence related injuries and deaths mostly caused by firearms are a major problem throughout the world. Understanding the factors that control the extent of hard-soft tissue wound patterns using computer imaging techniques, numerical methods, and machine learning algorithms may help physicians to diagnose and treat those injuries more properly. Here, we investigate the use of computational results coupled with the pattern recognition algorithms to develop an approach for forensic applications. Initially, computer tomography (CT) images of the patient whose leg was shot by a 9 × 19 parabellum bullet are used to construct the FE models of that patient’s femoral bone and the surrounding soft tissues. Then, Hounsfield units-based material properties are assigned to elements of the bone. To simulate the full range of loading conditions encountered in ballistic events, a constitutive model that captures the strain-rate dependent response is implemented. The entrance pathway vector of the bullet is directed in accordance with the patient’s wound and the simulations are deployed for the cases having various inlet velocities such as 150, 200, 250, 300, and 350 m/s. Once the FE results for each case are obtained, they are processed with supervised machine learning algorithms to classify the wound and inlet velocity correspondence. The results demonstrate that they can be diagnosed with a percent accuracy of 97.3, 97.5, and 98.3 for the decision tree (DT), k-nearest neighbors (kNN) and support vector machine (SVM) classifier, respectively. This approach may provide a useful framework in classifying the wound type, predicting the bullet impact velocity and its firing distance.