Towards universal early screening for cerebral palsy: a roadmap for automated General Movements Assessment
- PMID: 40735348
- PMCID: PMC12304702
- DOI: 10.1016/j.eclinm.2025.103379
Towards universal early screening for cerebral palsy: a roadmap for automated General Movements Assessment
Abstract
Cerebral palsy (CP) is the most common lifelong physical disability, affecting millions globally. Early detection and intervention are crucial for improving outcomes, yet many children are diagnosed late. The General Movements Assessment (GMA) is a highly accurate clinical tool for detecting infants at high probability of CP, but access to health professionals trained in the GMA limits its use. Artificial intelligence (AI) has the potential to automate the GMA, increasing accessibility worldwide. We established an interdisciplinary, international consortium for the purpose of developing a roadmap for the ongoing development and implementation of an AI-enabled GMA system for universal CP screening worldwide. The consortium included clinicians (children neurologists, paediatricians, neonatologists, allied health), researchers, engineers, computer scientists, legal experts, and individuals with lived experience, from around the globe (across Africa, Australia, Europe, and North America). The roadmap identifies the following steps and key requirements within: (1) development of standards for AI validation; (2) development of AI-GMA from large and diverse validation sets; (3) development of software tools and clinical pathways; (4) regulatory requisites; and (5) implementation. With the roadmap, AI-enabled screening for CP incorporating state-of-the-art technology can be made possible. Future work will require international collaboration to allow for scaling of data sets, refining automated solutions and translation into practice.
Funding: Cerebral Palsy Foundation, Cerebral Palsy Alliance, European Union Born to Get There, the National Health and Medical Research Council.
Keywords: Artificial intelligence; Cerebral palsy; Early detection; General movements; Machine learning.
© 2025 The Author(s).
Conflict of interest statement
AJS, PBM, AB, AG, CM, and CP are all members of the General Movements Trust. Norwegian University of Science and Technology (NTNU) and St. Olavs Hospital, Trondheim University Hospital, may benefit financially from a commercialization of the In-Motion AI-tools through existing intellectual properties which may include LA. All other authors declare no competing interests.
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References
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