Computer aided diagnosis of neurological damage to improve care for infants born prematurely

Lead Research Organisation: Imperial College London
Department Name: Dept of Computing

Abstract

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.

Planned Impact

The primary beneficiaries of the proposed research will be infants born prematurely as well as their parents and carers. The secondary beneficiaries will be clinicians, radiographers and nurses involved in neonatal care. The recent rise of pre-term births is a growing public health problem and has significant consequences for both the families concerned and society in general. In the UK 7.6% of live births are preterm; 23% of infants born at less than 26 weeks have severe disability and 41% demonstrate cognitive defects at 6 years of age. A significant number of preterm infants require long-term care, but targeting follow-on services to children who need them is difficult, particularly as early prediction of neurodevelopmental impairment is currently inaccurate. Not all families receive the right support, and unsurprisingly, unplanned healthcare usage is high, with hospital in-patient admissions, in-patient days and costs over the first 10 years after birth respectively 130%, 77% and 443% higher than for term infants.Recently, the Lancet, commenting on the 'growing and neglected problem' of preterm birth noted that 'health-care providers will have to be prepared to meet their long-term health problems which can include cerebral palsy, language and learning disabilities'. The National Audit Office noted the rising demand for neonatal services and recommended a 'whole system' approach underpinned by targeted research. The benefits of this project include many different aspects: Firstly, the research will contribute toward a significantly improved understanding of premature birth by defining the anatomical neural correlates of neuropsychiatric impairment. Identifying these correlates is essential for understanding how preterm birth adversely alters brain development. It is therefore a prerequisite for developing and evaluating preventative or therapeutic strategies. Secondly, the research will improve patient care by enabling earlier detection of abnormal brain development. This early detection is crucial to the use of preventative strategies for reducing additional future brain injuries. Finally, the ability to predict outcome offers the scope for improved parental counseling which is crucial for families affected by preterm birth.

Publications


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Ball G (2012) The effect of preterm birth on thalamic and cortical development. in Cerebral cortex (New York, N.Y. : 1991)
Ball G (2014) Rich-club organization of the newborn human brain. in Proceedings of the National Academy of Sciences of the United States of America
Ball G (2013) The influence of preterm birth on the developing thalamocortical connectome. in Cortex; a journal devoted to the study of the nervous system and behavior
 
Description 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.

In this project we have created a number 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 has been possible. The project has combined 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 demonstrated the potential to provide useful and descriptive models of the underlying anatomy that can be used for comparisons across subjects and over time. In this project we have learned patterns of normal and abnormal brain development. This has improved our ability to predict neurodevelopmental outcome in later life.
Exploitation Route The results from this project may be used to build improved computer-aided diagnosis systems to support clinicians in interpreting MR images from infants born prematurely. At the same time tools developed in this project can be applied to other neurological diseases such as dementia.
Sectors Healthcare
 
Description Several of the tools developed in this project are now used by clinical researchers in order to evaluated their usefulness.
First Year Of Impact 2013
Sector Healthcare
Impact Types Societal
 
Description ERC Synergy Grant
Amount € 3,250,000 (EUR)
Organisation European Research Council (ERC) 
Sector Public
Country European Union (EU)
Start 09/2013 
End 08/2020
 
Title Brain-development.org 
Description The database consist of computational atlases of brain development and the corresponding models 
Type Of Material Database/Collection of data 
Year Produced 2012 
Provided To Others? Yes  
Impact A large number of research groups worldwide are using the atlases of brain development provided here. This has generated more than 200 citations. 
URL http://www.brain-development.org/