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2019 Projects

On Tuesday 8 October 2019 we welcomed students and potential supervisors to the CDT’s first Sandpit event.

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The event consisted of talks from a range of academic and clinical supervisory teams who showcased their project ideas and research interests to CDT students.

Over 35 projects were presented across the day. Following the event students meet with supervisory teams they are interested in working with as they decide what projects they would like to pursue for their MRes/PhD.

Find below a list of projects presented this year:

Title 

Supervisors

Real-time treatment planning for transcranial  ultrasound therapy using deep learning 

Bradley Treeby, Ben Cox

Text Analytics for determination of natural history and phenotypes of paediatric disease -GOSH DRIVE

Richard Dobson, Neil Sebire

Extracting Bacteraemia risk Scores from Medical Notes to support ML based early warning system -UCLH CRIU

Richard Dobson, Neil Sebire

Chatbots for sexual health advice

Henry Potts, Julia Bailey

Simulation studies of bias

Julie Geogge, Henry Potts 

AI for Patient Flow at Great Ormond Street Hospital

Ben Margetts, Neil Sebire, Mario Cortina Borja

Automated lesion detection in focal epilepsy using MRI-EEG

Gareth Barnes,Konrad Wagstyl 

Improving diagnosis and predicting disease progression in childhood brain tumours using artificial intelligence

Chris Clark, Patrick Hales

Heart transplantation-survival prediction

Ami Banerjee, Dr Riyaz Patel, Mihaela van der Schaa,  Suliang Chen, Stephen Pettit

Medical student and junior doctor use of AI

Ami Banerjee & Rose Luckin & Mutli Cukurova

Student will explore the use of different networks (U-net, recurrent neural networks, generative adversarial neural networks, denoising autoencoders) to remove artefacts from cardiac MRI data

Vivek Muthurangu

AI and imaging in ophthalmology

Daniel Alexander, Pearse Keane 

The overall goal of the MSc project is to stratify patients suffering from non-small cell lung cancer (NSCLC) based on the outcome predicted from an artificial intelligence algorithm trained on a large and retrospective dataset of patients.

Gary Royal, Crispin Hiley, Charles-Antoine Collins-Fekete

The central aim of this work is to address whether classification based upon the gut microbiome can aid clinical diagnoses in the early stages to facilitate access to clinical trials. 

Nikhil Sharma

Non-linear methods to assess continuous physiological data from infants undergoing surgery

Cristine Sortica da Costa

Developing data driven algorithms to identify children with multimorbidity in NHS databases

Pia Hardeli , Daniel Alexander

High-dimensional counterfactual modelling  of lesion-deficit relations in focal brain injury

Parashkev Nachev, Ashwani Jha

Deep generative modelling of behaviour

Parashkev Nachev

Forecasting and Transfer Learning for Influenza-Like Illness (ILI) Rates Using Web Search Data

Ingemar Cox, Simon Moura and Vasileios Lampos

The Advanced Pathogen Diagnostics Unit and i-SENSE proposal

Eleni Nastouli 

AI Application in Pharmacogenomics

Chiara Bacchelli, Phil Beales

Personalised cancer medicine: Machine learning for predicting cancer prognosis and sensitivity to metabolism-altering drugs

Alvina Lai

Critically ill children with heart disease: can machine learning enhance vital sign pattern recognition and hence improve timely identification of deterioration? 

Katherine Brown, Simon Arridge

Novel approaches for characterising reproductive trajectories using electronic health data 

Katie Harron,  Arturo Gonzalez Izquierdo, Ruth Gilbert

Artificial Intelligence in “keyhole” brain surgery

Hani J Marcus, Danail Stoyanov, Rob Brownstone

Integrating multiple complex omics datasets to understand cardiovascular risk in autoimmune women

Ines Pineda-Torra, Elisabeth Jury, Dionisio Acosta

Supporting GP Practices through Chatbots

Fiona Stevenson, Fiona Hamilton, Enrico Costanza

Development of Machine Learning Techniques for Rapid Reconstruction of Abdominal Magnetic Resonance Images

Jennifer Steeden, Vivek Muthurangu

CCHIC  trajectories

Steve Harris 

Dynamic treatment strategies

Steve Harris 

Augmenting Clinical Decision Making in Intensive Care (ACaDeMIC) - Fluids

Samiran Ray 

Augmenting Clinical Decision Making in Intensive Care (ACaDeMIC) - Extubation

Samiran Ray 

Predicting blood product usage and automatic blood product ordering

Wai Keong Wong, Mallika Sekhar, Dave Roberts

TOMCAT Project for incidental findings

Riyaz Patel, Amand Schmid

The use AI on electronic medical records from primary care to develop tools for predicting adverse effects of commonly prescribed medication. 

Elizabeth Murray

Deploying digital technologies for HIV in rural South Africa

Valerian Turbe

Please see below a list of chosen projects:

Anthony Bourached

Deep Generative modelling of Behaviour

Parashkev Nachev

Abigail East

Developing data driven algorithms to characterise children with multimorbidity in NHS databases

Mario Cortina-Borja

Andre Vauvelle

Application of Signature Methods for Heart Failure Prediction with UK EHR data

Spiros Denaxas

Dominic Giles

High-dimensional counterfactual modelling of lesion-deficit relations in focal

brain injury

Ashwani Jha

Joanne Sheppard

Treatment planning for transcranial ultrasound therapy using deep learning

Bradley Treeby

Joseph Farrington

AI-enabled Blood Transfusion System

Dr. Wai Keong Wong

Peter Woodward-Court

Counterfactual Visual Explanations in Ophthalmic Imaging

Prof. Daniel Alexander

Thilina Jayatilleke

Predicting the need for mechanical ventilation in critical care patients with early respiratory failure using machine learning techniques

Prof. Steve Harris

Roy Shwartz

Deep learning, large datasets, genome-wide analysis to determine novel pathways in the progression of age-related macular degeneration

Adnan Tufail