Role of technologies in the recognition of general movements in neonates up to 20 weeks post-term

Abstract

The assessment of General Movements (GMs) has been used in clinical practice for the early detection of neurological disorders in neonates; however, its application requires a high level of expertise, time, and resources. Given these limitations, video-based technologies have emerged as an alternative approach for optimizing this evaluation. The objective of this Rapid Review is to update the role that technologies play in recognizing general movements and their relationship with the generation of clinical diagnoses. Four databases were reviewed (PubMed, Scopus, Web of Science, and BVS) following the PRISMA methodology. Studies focusing on neonates up to 20 weeks post-term that linked GM assessment with video-based and technology-assisted approaches, published between 2019 and 2023 in English, were included. A total of 30 studies were selected based on their methodological design and type of technology. The findings show that technological approaches, especially those based on deep learning and Artificial Intelligence (AI), contribute to earlier detection of neurodevelopmental disorders, are associated with the creation of automated databases, and improve access to assessment. The main benefits identified include optimization of clinical care, cost reduction, greater comfort for users and healthcare professionals, and improved opportunities for early intervention. In conclusion, technology applied to the assessment of general movements represents a significant advancement in the field of pediatric neurorehabilitation, supporting diagnostic processes, timely treatment, and facilitating implementation across diverse clinical settings.