Analyse Informatisée de la Motricité spOnTanée cHEz les nouRrIssons Néo

To date, the assessment of motor skills and posture in infants is based on a clinical examination performed by a doctor or, at best, a paediatrician. The reliability of this assessment is directly linked to the practitioner's experience, which can vary enormously and is often called into question in the case of atypical presentations. However, this is a very early and very frequent sign in many diseases as varied as serious infections, metabolic or neuromuscular metabolic or neuromuscular diseases. Delays in diagnosis can have irreversible consequences on development and health, which translate directly into mortality and morbidity.

The value proposition of the AI MOTHER project lies in its ability to provide a rapid, accurate, non-invasive and reliable assessment of the neurological and vital condition of infants, non-invasive and reliable assessment of the neurological and vital condition of infants. Using 2D videos obtained from consumer equipment, our system can detect early signs of motor and neurological disorders in infants, thanks to the use of computer vision and machine learning techniques. machine learning techniques.

This approach makes it possible to guide patients in the early stages of development of the disease. This orientation is decisive in patient management and enables early intervention to improve patient prognosis and reduce the costs associated with associated with pediatric healthcare.

Our aim is to industrialize this computerized infant motor analysis system and enable early detection of most neurological, infectious or metabolic diseases in infants. infant.

Patents
EP23306425.2 + PCT