Public Funding for Aiatella Ltd

Cardiovascular diseases (CVD) such as heart attack and stroke account for nearly 1 in 4 deaths in the UK. The North East region has a higher than national average CVD rate, with 590 deaths each month. Health inequalities are a major contributor to this statistic. A major risk factor for stroke is the presence of plaque (buildup of fats, cholesterol, and other substances) in the carotid arteries, which supply blood to the brain. This plaque often does not cause symptoms until it is advanced, and the risk of stroke is very high. If this plaque is identified early, lifestyle changes, medications and/or surgery can be performed to prevent stroke. Ultrasound imaging is an effective method for visualising plaque. It is non-invasive, relatively cheap, does not require radiation, and can be performed in outpatient settings, making it a viable option for preventive screening of stroke risk on a large scale. This screening can save lives and reduce the economic costs associated with treatment. However, interpretation of ultrasound imaging requires manual measurements performed by experienced healthcare professionals. This method is highly subjective and often results in variable outcomes. Our solution, AIATELLA, is a deep learning tool that can detect, measure and assess the makeup of plaque automatically. This enables healthcare professionals to make accurate and reliable decisions at the point of care. With ultrasound and the AIATELLA AI tool, current limitations of widespread ultrasound screening are removed, and preventive healthcare becomes a real possibility. In this project, we will train the AI tool with ultrasound images and evaluate its performance compared to experienced healthcare professionals. Once we have validated that the tool is accurate and reliable, in collaboration with Health Innovation NENC, we will take the ultrasound and tool into underserved communities, where access to healthcare is limited. Volunteers will be screened and the tool will automatically analyse any presence of plaque. This will be summarised with a risk score and those requiring follow-up will be directed to the appropriate care pathway. We will organise Patient and Public Involvement meetings during the project to align the project with community needs. The results of the project will be analysed and shared in a scientific journal. An impact report will be presented to NHS England, recommending the creation of a preventive stroke screening program. With this project, we aim to automate plaque analysis, improve patient care, and contribute to a healthier North East England.