Sarah Smith

Research Topic: Coverage measure and navigation strategies for robotic colonoscopy: Fusing machine learning and robotics to enhance diagnosis and treatment

Supervisors: Prof Pietro Valdastri, Dr Venkat Subramanian

About Sarah: I have a BSc and MChem in Chemistry from Leeds and currently finishing up my MSc in Computer Science from Nottingham. My project was on using support vector machines for Alzheimer’s disease classification. My current research interests are mainly in medical diagnosis using biomedical imaging and treatment.

Project description: Over the last decade, the STORM Lab (https://www.stormlabuk.com/) has developed the Magnetic Flexible Endoscope (MFE), an innovative platform for easy and painless colonoscopy. In conventional colonoscopy, 1 in 20 polyps are not detected during visual exploration due to the unstructured environment (e.g., tissue folds). Moreover, the navigation is difficult due to tight turns and obstacles. The project will explore the use of machine learning to integrate the localisation information provided by the MFE platform with the visual information produced by the endoscopic camera, in order to (1) estimate the total percentage of the colon visualised and (2) generate a navigation strategy to increase the precision of locomotion and endoscopic tasks such as biopsy.