AI Tool Enhances Forensic TBI Investigations

Oxford researchers have developed an AI-driven forensic tool to help police accurately assess traumatic brain injuries, enhancing investigations by predicting injury outcomes based on impact data and forensic reports.

A team of researchers from the University of Oxford, in collaboration with Thames Valley Police, the National Crime Agency, and medical institutions, has developed an AI-driven forensic tool to enhance investigations into traumatic brain injuries (TBI).

The study introduces a physics-based machine learning framework designed to help police and forensic teams accurately assess whether an impact caused a reported injury. TBI remains a critical public health issue with serious long-term consequences, yet forensic investigations currently lack a standardized method to quantify impact severity.

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Lead researcher Professor Antoine Jérusalem from Oxford’s Department of Engineering Science stated, “By leveraging AI and physics-based simulations, we provide law enforcement with an unprecedented tool to assess TBIs objectively.”

The AI model was trained using 53 anonymized police reports and forensic data, achieving remarkable accuracy:

• 94% for skull fractures
• 79% for loss of consciousness
• 79% for intracranial hemorrhage

The framework integrates a computational model of the head and neck with forensic data, including victim characteristics such as age and height. It does not replace human forensic experts but offers objective injury probability assessments, aiding investigations and improving risk assessments.

Dr. Michael Jones, forensic researcher at Cardiff University, emphasized the significance of the study, noting that forensic medicine has long struggled with linking injury mechanisms to outcomes. Sonya Baylis from the National Crime Agency highlighted that this technology will enhance forensic interpretations to support legal prosecutions.

While the model cannot identify perpetrators, it serves as a powerful tool for law enforcement, improving accuracy and consistency in TBI investigations. The research team hopes it will contribute to better injury prevention strategies and stronger legal evidence in assault cases.