PalsyVue addresses a critical gap in neonatal healthcare — the lack of effective, scalable tools for early detection of Cerebral Palsy (CP), one of the most common neurodevelopmental disorders, affecting approximately 1 in 450 births. Today, CP is typically diagnosed only 12–24 months after birth, when symptoms become visible and when therapeutic interventions are far less effective. This delay significantly reduces children’s chances for optimal development and places a heavy burden on healthcare systems and families.
To solve this problem, PalsyVue is developing an AI-driven diagnostic platform that uses computer vision and machine learning to identify early signs of CP based on the analysis of short infant movement videos.
The system will work as a Software as a Medical Device (SaMD), providing clinicians and parents with an objective, non-invasive, and affordable tool for early diagnosis.
The main goal of the project is to enable mass, precise, and early detection of CP, allowing for timely therapeutic intervention during the sensitive period of neurodevelopment (the first 6–18 months of life).
The planned activities include:
– developing and validating the diagnostic algorithm in collaboration with leading neonatology experts,
– ensuring regulatory compliance and CE certification under EU Regulations
– preparing for market entry through pilot implementations in clinical and home environments, and
– scaling the solution via a SaaS model for B2B, B2F, and B2G markets.
The milestones achieved so far include completion of a feasibility study confirming the ability to detect CP from 2D video recordings, and the development of a early functional prototype.
The next stages focus on technological development, clinical validation, device registration, and commercial rollout.