Application of AI in Oculomics
Research type
Research Study
Full title
Predicting circulatory mortality and morbidity using deep learning and retinal imaging
IRAS ID
341777
Contact name
Mervyn G Thomas
Contact email
Sponsor organisation
Leicester General Hospital
Duration of Study in the UK
3 years, 0 months, 30 days
Research summary
Cerebrovascular diseases (CBVDs), which include strokes, are a significant global health concern, causing illness, death, and disability. In the UK alone, strokes affect many people each year, with about 1.3 million stroke survivors living with its effects. Detecting these conditions early and identifying who is at risk is crucial for effective treatment and prevention.
Recent advancements in medical imaging, particularly using retinal scans, allow for early detection of disease by assessing the blood vessels in the eye.
Artificial intelligence (AI) and machine learning (ML) are being applied to analyze retinal scans more effectively. These technologies can quickly and accurately process large amounts of images, aiding in diagnosing various eye diseases. Moreover, they're being developed to predict broader health issues, such as the risk of CBVDs, based on features in retinal scans.However, there are challenges, such as ensuring AI models work well for diverse groups of people and addressing concerns about privacy and data quality.
Overall, integrating AI and ML with retinal scans could lead to earlier detection and better management of vascular diseases, potentially reducing their impact on individuals and healthcare systems. Researchers are working on developing predictive models that combine retinal features with other risk factors, aiming to create a tool that's affordable, non-invasive, and more effective than current methods.
REC name
London - City & East Research Ethics Committee
REC reference
24/PR/1409
Date of REC Opinion
3 Dec 2024
REC opinion
Favourable Opinion