TY - JOUR
T1 - Towards universal early screening for cerebral palsy
T2 - a roadmap for automated General Movements Assessment
AU - Spittle, Alicia J.
AU - Marschik, Peter B.
AU - Adde, Lars
AU - Badawi, Nadia
AU - Byrne, Rachel
AU - Bos, Arend F.
AU - Chatelin, Alain
AU - Coughlan, John
AU - Fedeli, Francesca
AU - Guzzetta, Andrea
AU - Ho, Edmond S.L.
AU - Johnson, Michelle J.
AU - Kwong, Amanda
AU - McEwan, Alistair
AU - Morgan, Catherine
AU - Mughogho, Anderson
AU - Murray, Deirdre M.
AU - Orlandi, Silvia
AU - Peyton, Colleen
AU - Prosser, Laura A.
AU - Ritterband-Rosenbaum, Anina
AU - Tran, Truyen
AU - Zhang, Dajie
AU - Passmore, Elyse
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2025/8
Y1 - 2025/8
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cerebral palsy
KW - Early detection
KW - General movements
KW - Machine learning
UR - https://www.scopus.com/pages/publications/105011170279
U2 - 10.1016/j.eclinm.2025.103379
DO - 10.1016/j.eclinm.2025.103379
M3 - Review article
AN - SCOPUS:105011170279
SN - 2589-5370
VL - 86
JO - eClinicalMedicine
JF - eClinicalMedicine
M1 - 103379
ER -