Automated Fetal and Neonatal Movement Assessment for Very Early Health Assessment
Lead Research Organisation:
Imperial College London
Department Name: Computing
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.
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.
Planned Impact
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.
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.
Publications
Schultes MT
(2022)
Barriers and Facilitators for Conducting Implementation Science in German-Speaking Countries: Findings from the Promote ImpSci Interview Study.
in Global implementation research and applications
Tan J
(2022)
Detecting Outliers with Foreign Patch Interpolation
in Machine Learning for Biomedical Imaging
Matthew J
(2022)
Exploring a new paradigm for the fetal anomaly ultrasound scan: Artificial intelligence in real time.
in Prenatal diagnosis
Liu T
(2022)
Video Summarization Through Reinforcement Learning With a 3D Spatio-Temporal U-Net.
in IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Iwaya LH
(2023)
On the privacy of mental health apps: An empirical investigation and its implications for app development.
in Empirical software engineering
Khatib N
(2023)
BORS/BJR TRAVELLING FELLOWSHIP ABSTRACT: MOTION CAPTURE OF NEONATAL INFANT KICKING MOVEMENTS CAN PROVIDE AN EARLY PREDICTION OF CEREBRAL PALSY
in Orthopaedic Proceedings
Cao Y
(2023)
Topological information retrieval with dilation-invariant bottleneck comparative measures
in Information and Inference: A Journal of the IMA
Description | We have developed a novel method that is able to estimate 2D and 3D poses of infants from 2D videos recorded by off-the-shelf standard hardware (e.g. a mobile phone camera). This method learns how to deform a very simple template to new data without human generated labels. Manual annotations are often a bottleneck for learning pose estimation models with machine learning. Our work shows a method that can learn this task with minimal human input. This method allows high-throughput movement analysis of infants without the need for special hardware or calibration. A simple cot-side camera is sufficient. |
Exploitation Route | These methods can be used by any application that aim for pose tracking of anything that can be modelled with a skeleton. Applications in support for handicapped patients, wild-life monitoring, sports, etc. are imaginable. The code and models will be available on-line after potential protection of the invention. |
Sectors | Creative Economy,Education,Environment,Healthcare,Leisure Activities, including Sports, Recreation and Tourism,Manufacturing, including Industrial Biotechology |
Description | King's College London |
Organisation | King's College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | algorithm development and link to external tracking |
Collaborator Contribution | additional data collection |
Impact | none yet but we plan joint publications |
Start Year | 2019 |
Description | TU Munich |
Organisation | Imperial College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | We are working on geneal image registration algorithms |
Collaborator Contribution | A visiting PhD research from TUM spent some time with our team at Imperial |
Impact | Work in progress. |
Start Year | 2020 |
Description | UKER Erlangen |
Organisation | Friedrich-Alexander University Erlangen-Nuremberg |
Country | Germany |
Sector | Academic/University |
PI Contribution | new potential testing clinic in the children clinic at UKER Erlangen with Prof. Regina Trollmann |
Collaborator Contribution | none yet, we are currently working out how we can continue this project together |
Impact | none yet |
Start Year | 2022 |