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Full NameProfessor Kathleen Curran
Diagnostic Imaging
University College Dublin
Webpage:ucd-ml-mi.com
Email Address:Email hidden; Javascript is required.
- bioengineering/medical devices
- artificial intelligence/machine learning/data analytics
- Medicine
- Surgery
- Psychiatry
- Anaesthetics
- Emergency Medicine
- General Practice
- Obstetrics and Gynaecology
- Ophthalmology
- Paediatrics
- Pathology
- Radiology
- Sports and Exercise Medicine
- Veterinary Medicine
- Dentistry
- Oral and Maxillofacial Medicine
- Rehabilitation Medicine
- Cardiology
- Cardiac Surgery
- Clinical Trials
- Health Informatics
- Neurology
- Neuropsychiatry
- Oncology
- Orthopaedic surgery
- Radiology
- Respiratory Medicine
- Rheumatology
- Vascular Medicine
As director of the Machine Learning in Medical Imaging and Diagnostics Research Group in UCD and funded investigator in ML-Labs Science Foundation Ireland Centre for Research Training in Machine Learning, I can provide masters and PhD students in biomedical engineering and computer science to assist PhD students new to computer programming but with an interest in developing AI techniques for clinical diagnosis / prognosis. Within my Machine Learning in Medical Imaging Research group we are focused on developing innovative clinical solutions. We achieve this by developing state-of-the-art machine learning methods and have specific expertise in delivering interpretable solutions such that we can provide quantitative explanations for the outputs from our deep learning models.
Please see Belton et al. MIUA 2021 (Best Paper Award) for recent AI work applied to MSK analysis; Janik et al. SPIE 2021 for Cardiac AI and Leydon et al. (2021) for Oncology AI publications. We also have expertise on Enterprise Imaging and Big Data (McCarthy et al. 2021) and diffusion MRI (Curran, KM et al. Quantitative Metrics in DTI, Springer 2016).
In your current clinical specialty do you think there is potential to develop AI methods to support clinicians with diagnosis / prognosis? For example, to discover novel imaging biomarkers that could potentially help stratify patients at higher risk of declining rapidly or automate classification in differential diagnosis.
I have supervised 6 postdoctoral research fellows and 6 PhD students to successful completion. Several clinical supervisors have served on RSPs for my PhD students. It would be better to defer to recommending clinical co-supervision because that will depend on the clinical specialty area of interest of the applicant.