Artificial intelligence in precise surgical planning for adolescent idiopathic scoliosis: applications and challenges

Abstract

Adolescent idiopathic scoliosis (AIS) is the most common three-dimensional spinal deformity among adolescents, characterized by a coronal curvature of > 10°, often accompanied by vertebral rotation and abnormal sagittal alignment, which can lead to severe cardiopulmonary and neurological dysfunction. For moderate-to-severe or progressive AIS (e.g., Cobb angle ≥ 45°), surgical intervention is the primary treatment modality. However, traditional surgical planning relies heavily on surgeons’ clinical experience and is subject to substantial interobserver variability, making it difficult to fully meet the needs of personalized precision medicine. In recent years, advances in artificial intelligence (AI), particularly in machine learning and deep learning, have provided new methodological tools for precise surgical planning in AIS. This article reviews the main applications of AI in AIS surgical planning, including AI-assisted assessment and automated measurement of preoperative spinal deformities; personalized decision-making for surgical approaches and fusion levels; personalized biomechanical and finite element analysis (FEA)-based optimization of internal fixation devices; prediction of postoperative outcomes; and recent advances in 3D surgical simulation and navigation. Furthermore, this review examines the current limitations of translating AI models into clinical practice, such as data heterogeneity, the “black-box” nature of algorithms, and ethical and regulatory issues. It also discusses future directions, including multicenter federated learning and human-in-the-loop collaborative decision-making, and aims to provide a systematic overview to support intelligent and precise surgical treatment of AIS.

Keywords

Adolescent idiopathic scoliosis / artificial intelligence / precise surgical planning / machine learning / deep learning / clinical translation

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