AI-Powered Tool Revolutionizes Traumatic Brain Injury Investigations in Forensics

New AI-powered tool could enhance traumatic brain injury investigations in forensics and law enforcement

A team of researchers from the University of Oxford, in collaboration with Thames Valley Police, the National Crime Agency, the John Radcliffe Hospital, Lurtis Ltd. and Cardiff University, has developed an advanced physics-based AI-driven tool to aid the forensic investigation of traumatic brain injuries (TBI). The study, published today (26 Feb) in Communications Engineering, introduces a mechanics-informed machine learning framework to help police and forensic teams accurately predict TBI outcomes based on documented assault scenarios. 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, to improve the accuracy and consistency of TBI investigations. Lead researcher Antoine Jérusalem, Professor of Mechanical Engineering in the Department of Engineering Science at the University of Oxford said: '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 TBIs objectively.' The study's AI framework, trained on real, anonymised police reports and forensic data, achieved remarkable prediction accuracy for TBI-related injuries: 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. 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. 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. 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. Professor Jérusalem added: '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.' Ms Sonya Baylis, Senior Manager at the National Crime Agency who supported this research project, said: '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.' 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.' The study 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.

New AI-powered tool could enhance traumatic brain injury investigations in forensics and law enforcement

A team of researchers from the University of Oxford, in collaboration with Thames Valley Police, the National Crime Agency, the John Radcliffe Hospital, Lurtis Ltd. and Cardiff University, has developed an advanced physics-based AI-driven tool to aid the forensic investigation of traumatic brain injuries (TBI). The study, published in Communications Engineering, introduces a mechanics-informed machine learning framework to help police and forensic teams accurately predict TBI outcomes based on documented assault scenarios. 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 standardized, quantifiable approach to doing this. The new study demonstrates how machine learning tools informed by mechanistic simulations could provide evidence-based injury predictions, to improve the accuracy and consistency of TBI investigations. Lead researcher Antoine Jérusalem, Professor of Mechanical Engineering in the Department of Engineering Science at the University of Oxford, said, "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 TBIs objectively." The study's AI framework, trained on real, anonymized police reports and forensic data, achieved remarkable prediction accuracy for TBI-related injuries: 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. The researchers trained the overall framework on 53 anonymized 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. 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. 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. Professor Jérusalem added, "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." Ms. Sonya Baylis, Senior Manager at the National Crime Agency who supported this research project, said, "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." 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." The study 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.