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

Basit Öğe Kaydı
dc.authoridOZAN, FIRAT/0000-0002-2417-8343
dc.authorscopusid36716504700
dc.authorscopusid36551628300
dc.authorscopusid55558645700
dc.authorscopusid7004704049
dc.authorwosidOZAN, FIRAT/ABA-8872-2021
dc.authorwosidyıldız, hasan/GSN-9276-2022
dc.contributor.authorPekedis, Mahmut
dc.contributor.authorOzan, Firat
dc.contributor.authorKoyuncu, Semmi
dc.contributor.authorYildiz, Hasan
dc.date.accessioned2023-01-12T20:03:46Z
dc.date.available2023-01-12T20:03:46Z
dc.date.issued2022
dc.departmentN/A/Departmenten_US
dc.description.abstractViolence 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 x 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.en_US
dc.identifier.doi10.1177/09544119221086397
dc.identifier.endpage675en_US
dc.identifier.issn0954-4119
dc.identifier.issn2041-3033
dc.identifier.issue5en_US
dc.identifier.pmid35303774en_US
dc.identifier.scopus2-s2.0-85126767282en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage665en_US
dc.identifier.urihttps://doi.org/10.1177/09544119221086397
dc.identifier.urihttps://hdl.handle.net/11454/77726
dc.identifier.volume236en_US
dc.identifier.wosWOS:000773543700001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSage Publications Ltden_US
dc.relation.ispartofProceedings of The Institution of Mechanical Engineers Part H-Journal of Engineering in Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWound ballisticsen_US
dc.subjectinjury biomechanicsen_US
dc.subjectgunshot woundsen_US
dc.subjectthe finite element methoden_US
dc.subjectmachine learningen_US
dc.subjectpattern recognitionen_US
dc.subjectWoundsen_US
dc.subjectSimulationen_US
dc.subjectBallisticsen_US
dc.subjectModelen_US
dc.subjectBulletsen_US
dc.subjectThoraxen_US
dc.subjectImpacten_US
dc.subjectHeaden_US
dc.titleThe finite element method-based pattern recognition approach for the classification of patient-specific gunshot injuryen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu