Manuel Trucco

Manuel Trucco is a Professor in the Department of Computing, with a research focus on biomedical image analysis, particularly in retinal imaging and its applications in systemic diseases. His work integrates artificial intelligence, deep learning, and image processing to develop tools for assessing retinal vasculature and detecting biomarkers for conditions such as diabetes, cardiovascular disease, and dementia.

His research interests lie at the intersection of computer science and clinical medicine. He specializes in retinal vessel segmentation, tortuosity measurement, fractal dimension analysis, and automated assessment platforms like VAMPIRE. His studies leverage large datasets including the UK Biobank and Lothian Birth Cohort to correlate retinal microvasculature changes with systemic diseases. He applies both handcrafted features and deep learning models to enhance classification accuracy in retinal image analysis.

The trends in his recent publications reflect a strong emphasis on AI-driven diagnostics, automated retinal image analysis, and non-invasive biomarker discovery. His work spans technical algorithm development and clinical validation, targeting applications in diabetic retinopathy, coronary artery disease, Alzheimer's, and glaucoma. The integration of multi-resolution modeling, particle swarm optimization, and ultra-wide field imaging demonstrates innovation in both methodology and scalability.

• VAMPIRE: Vessel Assessment and Measurement Platform for Images of the Retina
• Research supported by extensive project funding from 2007–2024
• Active contributor to large-scale cohort studies (UK Biobank, Lothian Birth Cohort)
• Supervised 10 research students or early-career researchers
• h-index of 37 with over 5,800 citations

Manuel Trucco leads a research group focused on retinal image analysis and biomedical computing, contributing to the development of software platforms and AI models for clinical use. His team works on both fundamental image processing techniques and their translation into medical practice, particularly in primary and secondary care settings.