[Technology-driven management of early-onset scoliosis:new technologies and concepts].

Early-onset scoliosis (EOS) refers to spinal deformity occurring in children younger than 10 years of age. The main goals of EOS treatment are not only to correct spinal curvature, but also to preserve spinal and thoracic growth potential. In recent years, the rapid development and clinical application of robotic technology and artificial intelligence (AI) have driven a paradigm shift in EOS management from experience-based practice toward intelligent and data-driven care. Emerging technologies, including robotic navigation, virtual and augmented reality (AR), AI-based imaging analysis, wearable devices and smart braces, digital twin modeling, and AI-driven integrated decision support systems, have been progressively introduced into different stages of EOS management. Together, these technologies contribute to a comprehensive framework encompassing preoperative planning, intraoperative navigation, and postoperative monitoring. Robotic navigation and AR-assisted systems have demonstrated improved accuracy and safety in pedicle screw insertion; AI algorithms have shown high concordance with conventional methods in automated Cobb angle measurement and scoliosis classification; wearable devices and smart braces enhance treatment adherence and enable continuous monitoring; and AI-driven decision support systems facilitate integrated and intelligent clinical management through multimodal data analysis. Nevertheless, current clinical evidence supporting the application of these technologies specifically in EOS remains limited, and challenges related to data security, cost-effectiveness, and ethical considerations persist. Looking forward, with continued technological integration and the accumulation of high-quality clinical evidence, EOS management is expected to evolve toward a closed-loop care model characterized by precise preoperative planning, accurate intraoperative execution, continuous postoperative surveillance, and long-term outcome prediction.