| EPSRC Reference: |
EP/I000445/1 |
| Title: |
Computer aided diagnosis of neurological damage to improve care for infants born prematurely |
| Principal Investigator: |
Rueckert, Professor D |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Computing |
| Organisation: |
Imperial College London |
| Scheme: |
Standard Research |
| Starts: |
01 October 2010 |
Ends: |
30 September 2014 |
Value (£): |
1,023,477
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| EPSRC Research Topic Classifications: |
| Biomedical neuroscience |
Image & Vision Computing |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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16 Mar 2010
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Healthcare Partnerships
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Announced
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Summary on Grant Application Form |
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Preterm birth is a major cause of neuropsychiatric impairment in childhood and leads to significant long-term clinical, educational and social problems. The incidence of preterm birth and low birth weight has increased over the last decade in industrialised countries, and preterm delivery has a higher prevalence among the unemployed and poorly educated. The burden of impairment is considerable: about 10% of all infants born before 33 weeks of age develop cerebral palsy; over 30% have neurocognitive problems; and half of all surviving infants born at 25 weeks or less show neurodevelopmental impairment at 30 months of age. These problems persist into later life which can have devastating consequences for the individuals and their families. A major issue confronting clinicians who work with preterm infants and their families is the identification of infants who are most at risk for subsequent neurodevelopmental disability and who may benefit from early intervention services. Improved prediction of later handicaps has the potential immediately to improve the delivery of care for preterm infants and their families. At the same time, the improved diagnosis will also aid the growing search for specific treatments to reduce brain injury. Several promising approaches are under active investigation, all of which rely or would be aided by improved diagnosis of adverse outcomes. Currently, the early assessment of brain development in preterm infants and prognosis of outcome is heavily dependent on a subjective assessment of clinical and low resolution imaging data. The aim of this project is the creation of tools and algorithms that enable the detection and diagnosis of abnormal brain development based on high-resolution magnetic resonance imaging (MRI) information. By interpreting these images within an evidence-based statistical framework, a more complete and objective, evidence-based interpretation will be possible. The project will combine two emerging paradigms in computer and imaging science to address the challenge of identifying abnormal brain development and predicting outcome: Machine learning techniques and computational anatomy. In combination these approaches have the potential to provide useful and descriptive models of the underlying anatomy that can be used for comparisons across subjects and over time. This offers the possibility to learn patterns of normal and abnormal brain development and to predict the pattern of future brain development. The result of the research will be a significantly improved ability to predict neurodevelopmental outcome in later life. The ability to predict outcome improves parental counseling and selection of infants for early therapeutic strategies aiming at preventing or ameliorating cerebral injury.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.imperial.ac.uk |