ChairesIA_2019_1 - Chaires de recherche et d'enseignement en Intelligence Artificielle - vague 1 de l'édition 2019

AI for paedriatric neurorehabilitation – AI-4-CHILD

Submission summary

Artificial intelligence (AI), in its modern form, is profoundly changing many areas, including health. The development of AI for health opens up very promising prospects for improving the quality of care, reducing costs through more personalized care, but also better traceability and improved medical decision-making support. In the field of medical imaging, radiology (in the broad sense) is expected to undergo a significant evolution through the use of machine learning algorithms. The AI4Child project focuses on the development of new medical image analysis methods to assist in the diagnosis and follow-up of patients with cerebral palsy.

Cerebral palsy is the most common motor deficiency in children, with a prevalence of 2.1 cases per 1000 births. It affects 17 million people worldwide, and 125,000 in France. This non-progressive disorder causes abnormal patterns of movement and posture, and cognitive and sensory functions can also be impaired. These deficiencies result from structural abnormalities in the brain that occur at different times of eraly brain development. Symptoms are usually apparent before the age of 18 months and the diagnosis is usually confirmed between 13 and 19 months. However, it has been shown that earlier diagnosis would allow for better care of affected children. The objective of AI4Child is therefore to develop and promote new AI-based tools to improve the early diagnosis phase and also to enable monitoring of children's motor development through the emergence of dynamic Magnetic Resonance Imaging (MRI).

The core of the project will be based on a methodological development in the continuity of our work in deep learning, and in particular for the understanding of neural network architectures such as residual networks. These new tools will be adapted to the specific issues raised by the study of cerebral palsy, namely: early diagnosis based on cerebral MRI data from the premature baby, high-resolution reconstruction of dynamic in-vivo MRI sequences to measure biomechanical parameters of the child's movements, and finally assistance in rehabilitation through an analysis of walking augmented by in-vivo MRI data. This research work will be carried out at IMT Atlantique and the University Hospital of Brest, within the LaTIM laboratory, in partnership with Philips and the Ildys Foundation.

The new knowledge and tools resulting from this work will be disseminated within the scientific communities but also through teaching programmes to the varied public (doctoral students' research training, engineering school, medical school, international summer schools, tutorials). In order to strengthen the links between the various actors, we will continue our action within the European Academy of Childhood Disability (EACD) conference, and more particularly in the organisation of the hackathon bringing together families of disabled children, health professionals, students and researchers. The creation of such synergy in the child-centred ecosystem will promote the impact of the AI4Child initiative at the national and international levels, in research and teaching around AI and its application to health.

Project coordination


The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.



Help of the ANR 599,999 euros
Beginning and duration of the scientific project: August 2020 - 48 Months

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