MSc in Computer Science
DPhil Student | Research Scientist
Artificial Intelligence to improve Cardiometabolic Risk Evaluation using routine CT scans.
A highly motivated software engineer, committed to enhancing patient care by translating cutting edge AI technology into diagnostic medical imaging.
I did my masters in Computer Science from Oxford Brookes University mainly focusing on honing my AI and machine learning skills. I worked on an interesting project of differentiating between different neurological conditions (Parkinson's, Huntington's, Ataxia and Diabetes Mellitus) using IMU gait data as part of my dissertation project.
I have 5+ years of work experience mainly in software testing and automation field. I worked for 3 years with CGI Inc., India, where I developed more interest in AI and machine learning field. I joined Caristo Diagnostics Ltd. as a Software Engineer in September, 2019 to realise Professor Charalambos Antoniades’s ground-breaking cardiac imaging research into a medical software device that patients, doctors and researchers can benefit from worldwide.
My DPhil will concentrate on developing a risk prognostic model for early diagnosis of individuals at risk of diabetes or pre-diabetes using AI, radiomics and machine learning techniques from routine CT scans. Patients undergoing cardiac CT imaging may already have risk factors for pre-diabetes. Accurate detection of the condition in such patients can dramatically improve long-term survival. Inflammation of fat tissue (called adipose tissue) can be a critical factor in the major complications of diabetes, such as cardiovascular disease. Currently, none of the routine scans or tests can identify inflammation in fat tissue which could lead to risk of developing diabetes.
I have been awarded the 1851 Royal Commission Industrial Fellowship Award to carry out this research with the University of Oxford and Caristo Diagnostics under supervision of my academic supervisor, Prof. Charalambos Antoniades, and industrial supervisor, Dr. Pete Tomlins.
Using artificial intelligence to study atherosclerosis, predict risk and guide treatments in clinical practice.
Antoniades C. et al, (2023), Eur Heart J, 44, 437 - 439