Forensic AI tool aids brain injury investigations

Led by Oxford University, the study saw an AI model trained on real, anonymised police reports and forensic data from cases with traumatic brain injuries (TBIs). This resulted in a mechanics-informed machine learning framework to help police and forensic teams accurately predict TBI outcomes based on described assault scenarios. It achieved prediction accuracy exceeding 94 per cent for skull fracture, with 79 per cent for loss of consciousness and intracranial haemorrhage.

The work, published in Communications Engineering, featured input from Thames Valley Police, the National Crime Agency, Cardiff University, Lurtis Ltd., the John Radcliffe Hospital and other partner institutions.

“This research represents a significant step forward in forensic biomechanics,” said lead researcher Antoine Jérusalem, Professor of Mechanical Engineering at Oxford.

“By leveraging AI and physics-based simulations, we can provide law enforcement with an unprecedented tool to assess TBIs objectively.”

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. An AI layer then incorporates this information with any additional relevant metadata, such as the victim’s age and height, before providing a prediction for a given injury.