Test of a new method in the distinction between falls and blows on a postmortem CT-scans sample

Area of Science:

• Forensic Anthropology
• Forensic Pathology
• Medical Imaging

Background:

• Distinguishing between accidental falls and intentional blows is crucial in forensic investigations.
• Existing methods for fracture analysis can be limited, especially with incomplete skeletal remains.

Purpose of the Study:

• To develop and validate a novel method for discriminating between falls and blows using fracture patterns.
• To assess the accuracy of this method on post-mortem computerized tomography (PMCT) scans.

Main Methods:

• A database of 549 fracture types across 57 bones and 12 regions was compiled.
• Random forest models were trained and optimized using binary coding of anatomical data.
• The best-performing model was validated on a sample of 47 subjects with known fall or blow etiologies.

Main Results:

• The developed method demonstrated substantial reliability (Cohen's k=0.67) in distinguishing fall from blow injuries.
• High accuracy was observed for fall cases (97.2%), with a misclassification rate of 36.4% for blow cases.
• The method showed effectiveness even with fragmented or missing bones, correctly estimating etiology in 91.7% of such cases.

Conclusions:

• The new method provides a reliable approach for differentiating between falls and blows, applicable to fragmented remains.
• An accessible online tool (fracture.cloud) has been developed to facilitate the practical application of this method.