Details of Grant 

EPSRC Reference:	EP/S013687/1
Title:	Automated Fetal and Neonatal Movement Assessment for Very Early Health Assessment
Principal Investigator:	Kainz, Dr B
Other Investigators:	Kedgley, Dr AE Rueckert, Professor D Nowlan, Dr N Burdet, Professor E Glocker, Dr B
Researcher Co-Investigators:
Project Partners:
Department:	Computing
Organisation:	Imperial College London
Scheme:	Standard Research
Starts:	01 April 2019	Ends:	31 March 2022	Value (£):	625,543
EPSRC Research Topic Classifications:	Image & Vision Computing Med.Instrument.Device& Equip. Medical Imaging
EPSRC Industrial Sector Classifications:	No relevance to Underpinning Sectors
Related Grants:	EP/S013601/1
Panel History:	Panel Date Panel Name Outcome 30 Oct 2018 HT Investigator-led Panel Meeting - October 2018 Announced
Summary on Grant Application Form
The time during pregnancy and the first weeks and months after birth are critical for sensorimotor development, and a distinct change in fetal or neonatal movements can be an indicator of neurological or motor-system compromise. As is commonly known, fetuses and neonates move a lot spontaneously. The quantity and quality of these movements evolve significantly during early development, and clear deviations in this trajectory are thought to be an indicator of adverse neurodevelopment. In current clinical practice, fetal or neonatal movements are not systematically quantified, leading to under-diagnosis of conditions for which reduced or abnormal movements are the key characteristic. Fetal movements have been shown to be abnormal in the case of severe brain abnormalities (e.g., anencephaly) but there are also indications that neurological conditions such as autism and cerebral palsy (CP) result in abnormal movement signatures in very early life. This project addresses the clear need for an objective and automated means to quantitatively assess fetal and neonatal movement patterns, to facilitate earlier diagnosis and more effective treatments of life-changing conditions affecting babies and children. We will develop methods to track fetal and neonatal movements, use machine learning to elucidate links between specific movement patterns or characteristics of diseases, use computational modelling to provide understanding of movement signatures that are specific to particular illneses, and validate the efficacy of assessing movement patterns as a diagnostic tool. We will validate our movement assessment algorithms and apply them to diagnose brain development; primarily CP leading to follow-up research on autism and stroke/seizure. One in 200 children in the UK suffer from CP caused by pre- or perinatal brain damage, but a formal diagnosis (and therefore appropriate therapy at the most critical early stage of life) is not possible before 24 months. One reason for this is the lack of appropriate reproducible movement assessment techniques. Diagnosis of other common neurological conditions affecting neonates such as strokes/seizures is currently only possible with continuous electroencephalogram (EEG) monitoring and/or Magnetic Resonance Imaging (MRI) of the brain and spine, but such technologies are not always available in less well equipped neonatal wards, or in developing countries. Furthermore, accurate interpretation of EEG and MRI requires specialist expertise that is not always widely available. Our approach will democratise this expertise and make it widely available, which will improve quality of care significantly.
Key Findings
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Potential use in non-academic contexts
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Organisation Website:	http://www.imperial.ac.uk