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Getty, Akacin Phonsawat.
TBI is a critical public health issue, with severe and long-term neurological consequences. In forensic investigations, determining whether an impact could have caused a reported injury is crucial for legal proceedings, yet there is currently no standardised, quantifiable approach to do this. The new study demonstrates how machine learning tools informed by mechanistic simulations could provide evidence-based injury predictions. This would help police and forensic teams accurately predict TBI outcomes based on documented assault scenarios.
The study’s AI framework, trained on real, anonymised police reports and forensic data, achieved remarkable prediction accuracy for TBI-related injuries:
- 94% accuracy for skull fractures
- 79% accuracy for loss of consciousness
- 79% accuracy for intracranial haemorrhage (bleeding within the skull)
In each case, the model showed high specificity and high sensitivity (a low rate of false positive and false negative results).
The framework uses a general computational mechanistic model of the head and neck, designed to simulate how different types of impacts—such as punches, slaps, or strikes against a flat surface—affect various regions. This provides a basic prediction of whether an impact is likely to cause tissue deformation or stress. However, it does not predict on its own any risk of injury. This is done by an upper AI layer which incorporates this information with any additional relevant metadata, such as the victim’s age and height, before providing a prediction for a given injury.
Read the full story on the University of Oxford website.