Research Topic: Using pre-operative wearable sensors to develop a patient fitness algorithm that predict surgical outcomes amongst colorectal cancer patients.
Supervisors: Dr Zhi-Qiang Zhang, Professor David Jayne, Professor Alejandro Frangi
About Aron: I completed my Undergraduate degree at the University of Exeter studying BSc Hons Sports and Exercise science. From there I went on to UCL to complete an MSc in Population Health where I gained experience in several different research projects including investigating patients’ experiences of lung cancer diagnosis and care.
Project description: Bowel cancer is amongst the most common forms of cancer in the UK and the second biggest cancer killer. Patients often undergo surgery as part of their treatment plan and although surgery can be an effective treatment, major abdominal surgery is associated with substantial morbidity and mortality. Identifying these high risk patients will allow appropriate resource management in the hospital and can support clinicians in allocating appropriate care pathways. Cardio-pulmonary exercise testing (CPET) is the current gold standard for assessing pre-operative fitness and has been shown to predict postoperative complications. However, CPET is not suitable for patients with mobility impairments and is an expensive exercise to undertake. This project will be using data collected from a feasibility study at St James’ Hospital investigating whether a remote monitoring wearable biosensor can act as a cheaper and possibly more accurate detector of postoperative complications. Data collected from the patch will include ECG recordings, accelerometer, posture and skin temperature readings. We aim to employ both traditional Machine Learning and Deep Learning to predict surgical outcomes from physiological signals as well identifying important biomarkers.