The findings have been published today in Communications Engineering.
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.
AI and Machine Learning for TBI Predictions
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).
This research represents a significant step forward in forensic biomechanics. By leveraging AI and physics-based simulations, we can provide law enforcement with an unprecedented tool to assess TBI objectively.
How the AI Framework Works for Traumatic Brain Injury Investigations
Lead researcher Antoine Jérusalem, Professor of Mechanical Engineering in the Department of Engineering Science, University of Oxford, explained that 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.
The researchers trained the overall framework on 53 anonymised real police reports of assault cases. Each report included information about a range of factors which could affect the blow’s severity (e.g., age, sex, body build of the victim/offender). This resulted in a model capable of integrating mechanical biophysical data with forensic details to predict the likelihood of different injuries occurring.
Key Findings from the TBI Study
When the researchers assessed which factors had the most influence on the predictive value for each type of injury, the results were remarkably consistent with medical findings. For instance, when predicting the likelihood of skull fracture, the most important factor was the highest amount of stress experienced by the scalp and skull during an impact. Similarly, the strongest predictor of loss of consciousness was the stress metrics for the brainstem.
Understanding brain injuries using innovative technology to support a police investigation, previously reliant on limited information, will greatly enhance the interpretation required from a medical perspective to support prosecutions.
Collaboration and Expert Input for TBI Investigations
Ms Sonya Baylis, Senior Manager at the National Crime Agency, commented: “Understanding brain injuries using innovative technology to support a police investigation, previously reliant on limited information, will greatly enhance the interpretation required from a medical perspective to support prosecutions.”
The research team insists that the model is not intended to replace the involvement of human forensic and clinical experts in investigating assault cases. Rather, the intention is to provide an objective estimate of the probability that a documented assault was the true cause of a reported injury. The model could also be used as a tool to identify high-risk situations, improve risk assessments, and develop preventive strategies to reduce the occurrence and severity of head injuries.
Limitations of the AI Model in Traumatic Brain Injury Forensics
Lead researcher Antoine Jérusalem, Professor of Mechanical Engineering in the Department of Engineering Science at the University of Oxford, said: “Our framework will never be able to identify without doubt the culprit who caused an injury. All it can do is tell you whether the information provided to it is correlated with a certain outcome. Since the quality of the output depends on the quality of the information fed into the model, having detailed witness statements is still crucial.”
Dr Michael Jones, Researcher at Cardiff University, and Forensics Consultant, said: “An ‘Achilles heel’ of forensic medicine is the assessment of whether a witnessed or inferred mechanism of injury, often the force, matches the observed injuries. With the application of machine learning, each additional case contributes to the overall understanding of the association between the mechanism of cause, primary injury, pathophysiology, and outcome.”
Conclusion: The Future of Traumatic Brain Injury Forensics
The study ‘A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations’ has been published in Communications Engineering. It was conducted by an interdisciplinary team of engineers, forensic specialists, and medical professionals from the University of Oxford, Thames Valley Police, the National Crime Agency, Cardiff University, Lurtis Ltd., the John Radcliffe Hospital, and other partner institutions.
