A proposal for cut marks classification using machine learning: Serrated vs. non-serrated, single vs. double-beveled knives
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a standardized method and terminology for analyzing sharp force trauma on bones. Machine learning models accurately classify knife characteristics from cut marks, aiding forensic analysis.
Area Of Science
- Forensic Anthropology
- Forensic Science
- Bioarchaeology
Background
- Standardization is lacking in analyzing sharp force trauma on skeletal remains.
- Existing methodologies for cut mark analysis lack consistent terminology.
- Accurate identification of tool marks is crucial in forensic investigations.
Purpose Of The Study
- To present a standardized classification method for cut marks on human bones.
- To establish applicable methodology and terminology for sharp force trauma examination.
- To develop machine learning models for objective analysis of stab wounds.
Main Methods
- 350 cut marks were produced on pig ribs using seven different knives.
- Analysis involved stereomicroscopy with tangential lighting.
- Eleven key traits of cut marks were identified and used to train binary logistic regression models.
Main Results
- Eleven traits significantly associated with knife type were identified.
- Machine learning models achieved 63%-85% accuracy for serration classification.
- Models achieved 63%-89% accuracy for blade bevel classification.
Conclusions
- A novel set of traits for cut mark analysis has been proposed.
- Machine learning models can standardize and facilitate the analysis of stab wounds.
- This methodology enhances the objective examination of sharp force trauma in forensic cases.
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