In-Situ Mobile Application for the Triage of Pedestrians in Vehicle Collision (Sentinel)

Funder

Road Safety Trust

Value

£310,192

Project team

• Dr Christophe Bastien (Coventry University) - Principal Investigator
• Dr Vadhiraj Shrinivas (Coventry University) - Research Scientist
• Dr Raj Desai (Coventry University) - Assistant Professor, Injury Criteria
• Emilie Quinn (Coventry University) - Project Manager
• Dr Michelle Mayer (Coventry University) - Research Data Management Officer
• Prof Joseph Hardwicke (University Hospital of Coventry and Warwickshire) - Consultant Plastic & Reconstructive Surgeon
• Dr Caroline Leech (The Air Ambulance Services) - Consultant Emergency Medicine
• Andrew Rosser (West Midlands Ambulance Services) - Head of Research and Development

Duration

4th November 2024 – 3rd November 2027

Project overview

In the UK, in 2022, 376 pedestrians were killed and 19318 were injured, highlighting the important societal impact and a notable economical loss of £2.2M per death and £261,498 for each seriously injured victim. 8.5% of pedestrian victims are incorrectly triaged, due to the limitations of suitable tools. Emergency services can unintentionally direct pedestrian victims to the wrong hospital, especially when these victims do not appear to be severely injured at the scene of the collisions. In 3.8% of pedestrian collision cases, the victims died within 28 days of the collisions, and in 4.7% of the cases, victims sustained a serious (AIS3+) traumatic brain injury (TBI) which have for effect long-term injuries, affecting physical, cognitive, and psychological well-being.

The aim of SENTINEL is to create and test a new mobile triage application for ambulance and Emergency Department (ED) services to assess, by the roadside, adult pedestrian brain injuries (16yo+), faster, more accurately and efficiently than current NHS systems

Objectives

The SENTINEL mobile triage tool will be achieved by:

1. a computation phase using a state-of-the-art and free to download skull and brain human computer model from Toyota, and creating a computer routine to extract TBI against a historical pedestrian collision database;
2. converting this trauma extraction computer code into a fast and portable Machine Learning (ML) equivalent;
3. forging a stronger partnership between Emergency services and hospitals by creating, for the first time, new processes to capture photographic/ photogrammetry evidence at the collision and merge this information with the hospital Electronic Patient Record (EPR), creating a West Midlands pedestrian collisions dataset, and;
4. validate the ML computer programme with this West Midlands dataset before creating the final mobile application tool, which will be deployed and tested by ambulances for in-situ pedestrian TBI assessment.

Impact statement

• Providing best triage by the roadside, which will save lives and improve recovery
• Improvement to the computer modelling of brain injuries
• Design safer vehicles to pedestrians

Outputs 

SENTINEL-RTC – "In-Situ Mobile Application for the Triage of Pedestrians in Vehicle Collision"