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Future Solutions In Progress


Round 9: Theme menu with 5 topics / areas of special interests

  • Clinical applications of genetics
  • Early detection and diagnosis of chronic illness or long term conditions
  • Minimising the impact of trauma and serious injury
  • Informing clinical management through software-based analysis of complex datasets
  • Repurposing of medicines and medical devices

Round 9 Projects:

 

Automatic anomaly detection for brain imaging triage and classification
Automatic anomaly detection for brain imaging triage and classification
Principal Investigator:  Dr Parashkev Nachev
Organisation: University College London
Start Date 11th August 2014
End Date: 10th August 2017

View Abstract

Modern brain imaging contains vastly more information than historical radiographs, yet its clinically informative output has remained the same: a radiologist’s verbal report. As the information content of imaging increases, a void has opened between what we expensively collect and what we actually use.

This is both a lost opportunity, and an obstacle to the continued growth of brain imaging. Technology being developed by Dr Parashkev Nachev and colleagues at University College London seeks to close this gap by applying novel computer-assisted algorithms so as to exploit much more of the information in each scan than a verbal report contains. An automatic “anomaly map” for each scan, indexing the deviation from normality of each point, will assist radiological reporting, allow the application of computer systems that predict clinical outcomes from patterns of anomaly, and guide radiological triage and resource/performance management. The project aims to demonstrate the feasibility, robustness, clinical, and managerial value of the approach using a large collection of standard brain imaging, and to deliver a pilot system capable of translation into a full clinical product.

Without changing any clinical pathways or adding new investigations, the system will improve radiological reporting and optimise radiological triage and management, while creating a scalable major new platform for computational imaging analysis.

Automatic anomaly detection for brain imaging triage and classification
Applications of Next Generation sequencing in Newborn Screening
Principal Investigator:  Dr Ann Dalton & Professor Anne Goodeve
Organisation: Sheffield Children's NHS Foundation Trust
Start Date 1st March 2015
End Date: 30th June 2018

View Abstract

UK newborn babies are currently screened for five rare disorders using dried blood spots (DBS) taken shortly after birth and four further disorders will be added in 2015. Screening use biochemical tests performed on the DBS to identify babies affected with these conditions. Some “screen-positive” babies have genetic tests to confirm the disorder. The disorders being tested are potentially life-threatening but are treatable if identified early.

The severity of the disorders differs between patients. The link between genetic defects (mutations) and the disorder severity are not well understood To improve knowledge of the link between mutations and symptoms/severity, a team jointly lead by Dr Ann Dalton (Sheffield Children’s NHS Foundation Trust) and Prof Anne Goodeve (University of Sheffield) proposes to use a new technology, next generation sequencing (NGS) to find mutations in six screened disorders.

The researchers will collect details of patients’ symptoms, blood chemicals and mutations in a database to understand the links between them for each disorder. This will help provide more appropriate and personalised treatment to affected babies. They will also investigate whether NGS could be used to identify patients with disorders having no abnormal chemicals in the blood by creating a system to deliver rapid genetic results which could be used for NBS and other purposes.

Automatic anomaly detection for brain imaging triage and classification
The Application of Next Generation Sequencing (NGS) to the analysis of Minimal Residual Disease (MRD) in Childhood Acute Lymphoblastic Leukaemia (ALL)
Principal Investigator:  Dr John Moppett
Organisation: University Hospitals Bristol NHS Foundation Trust
Start Date 1st May 2015
End Date: 30th April 2018

View Abstract

Each year in the UK there are approximately 400 new cases of childhood ALL and 30 cases of relapsed ALL. It is really important if this treatment is to be successful to use a marker, known as Minimal Residual Disease (MRD) that indicates risk so that the treatment can be tailored to each child.

Although extremely helpful, the current method of measuring MRD is time consuming, expensive, complicated and doesn’t give a result for all children.

A team led by Dr John Moppett of University Hospitals Bristol NHS Foundation Trust is developing a new method that will enable delivery of a cheaper, faster and more sensitive test, involving more patients, whilst also enabling further understanding of why children with the disease sometimes relapse.