Automated Fetal and Neonatal Movement Assessment for Very Early Health Assessment

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering

Abstract

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.

Publications

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Description Collaboration with Chalmers University Sweden 
Organisation Chalmers University of Technology
Country Sweden 
Sector Academic/University 
PI Contribution We are collecting 3D camera and electro-magnetic tracking data which is being used to measure infant movements. We are collecting the data.
Collaborator Contribution Collaboration with Silvia Muceli who is an expert in human motor control and signal processing. She has post-doctoral fellow and a Marie-Curie fellowship to work on similar work so has been providing expertise about the associated analysis
Impact This is a multi-discplinary collaboration with a bioengineering group. This work has only started recently so there are no specific outputs yet.
Start Year 2019