Research Topic: Development of an Artificial Intelligence Toolkit to Extract Body Composition Parameters from Imaging and Explore their Prognostic Value for Defining Frailty in Multiple Myeloma
Supervisors: Prof Gordon Cook, Prof Andrew Scarsbrook, Prof Andy Bulpitt.
About Christopher: I graduated from Sheffield Hallam University in 2017 with an undergraduate degree in Mathematics. After this I worked as a data scientist for 4 years, primarily working with hospital data. Most recently, I spent a year on secondment working for the Joint Biosecurity Centre producing various Covid-19 models and analyses.
Project description: Over recent years, outcomes for multiple myeloma (MM) have dramatically improved for younger/fitter patients. However, the same successes have not been seen in elderly/less-fit patients, with one probable cause being that these patients can only receive a limited amount of effective therapy, leading to undertreatment. Identifying these patients is challenging, as current clinical frailty risk scores are often insensitive, flawed, time-consuming, subjective, and/or prone to sudden changes. A potential solution is to develop an artificial intelligence toolkit that can automate the extraction of body composition metrics from routinely acquired imaging data.