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Steiger, GS and Borrini, M (2024) A proposal for cut marks classification using machine learning: Serrated vs. non-serrated, single vs. double-beveled knives. Journal of Forensic Sciences. pp. 1-13. ISSN 0022-1198
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A proposal for cut marks classification using machine learning Serrated vs non serrated single vs double beveled knives.pdf - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview |
Abstract
In tool mark identification, there is still a lack of characteristics and methodologies standardization used to analyze and describe sharp force trauma marks on skeletal remains. This study presents a classification method for cut marks on human bones, providing an applicable methodology for their examination and the relevant terminology for describing cases of sharp force trauma. A total of 350 cut marks were produced by stabbing pig ribs (Sus scrofa) with seven knives. The samples were analyzed under a stereomicroscope with a tangential light source. Through the analysis of cut marks, eleven traits were identified as significantly associated with the type of knife used. These traits included the general morphology of the kerf shape, the entrance and exit cross-profile shapes, the location of the rising on the entrance and exit cross-profile, the presence or absence of feathering, the presence or absence of shards and the location and the general morphology of the mounding. Binary logistic regression models were later trained and tested using nine out of the eleven traits. The first model categorized the cut mark as either produced by a serrated or non-serrated blade, while the second, as either produced by a single- or double-beveled blade. Classification scores of those models ranged between 63%-85% for the serration class and 63%-89% for the blade bevel class. This study proposes a new set of traits and the use of machine learning models to standardize and facilitate the analysis of stab wounds.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; cut mark analysis; forensic anthropology; forensic pathology; machine learning; sharp force trauma; trauma analysis; 0399 Other Chemical Sciences; 0699 Other Biological Sciences; 1103 Clinical Sciences; Legal & Forensic Medicine |
| Subjects: | Q Science > QH Natural history > QH301 Biology |
| Divisions: | Biological & Environmental Sciences (from Sep 19) |
| Publisher: | Wiley |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 31 Jul 2024 12:34 |
| Last Modified: | 31 Jul 2024 12:45 |
| DOI or ID number: | 10.1111/1556-4029.15588 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/23842 |
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