Automated Fetal and Neonatal Movement Assessment for Very Early Health Assessment

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.

Who might benefit from this research?
To ensure maximal impact we will engage with
- healthcare professionals who might be impacted by this research which includes radiologists, public health professionals, general physicians, paediatricians, gynaecologists, physiotherapists, frontline health workers.
- Computer Scientists working in Machine Learning, Computer Vision, 3D reconstruction, high-performance computing and decision support systems.
- pregnant women and mothers and fathers with their infants
- healthcare technology companies all around the world.
- healthcare professionals interested in healthcare policy, public health and introduction of new healthcare interventions.
- biomedical engineers and clinicians who need standard models for comparison during diagnostics and to assess healthy movements.

How might they benefit from this research?
- the ultimate goal of the technology is to support front line health workers in identification of high risk babies;
- the developed models will serve as a baseline representation for healthy movements;
- Pregnant women and infants may benefit from quicker referral, access to antenatal care closer to home, and comfort (if not referred) that the pregnancy is not of concern. The system may also lead to earlier identification of problems before they become critical;
- Healthcare policy makers and professionals working in public health will be interested in understanding how this variant of motion modelling technology might have impact if introduced into clinical practice and home care.