Medical image analysis for three-dimensional reconstruction in adolescent idiopathic scoliosis: Imaging inputs, reconstruction methods, validation evidence, and research gaps.
Authors
Affiliations (3)
Affiliations (3)
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; Spine Surgery Center, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China. Electronic address: [email protected].
Abstract
Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional (3D) spinal deformity. Although standing full-spine radiography remains the cornerstone of clinical diagnosis and longitudinal follow-up, two-dimensional projections cannot fully characterize vertebral rotation, local osseous geometry, or global spatial alignment. In recent years, computed tomography (CT), magnetic resonance imaging (MRI), conventional radiography, EOS/biplanar radiography, 3D ultrasound, and surface imaging have increasingly been integrated with deep learning, statistical shape modeling, and generative approaches, substantially expanding the technical pathways for patient-specific 3D reconstruction in AIS. However, substantial heterogeneity remains across studies with respect to input constraints, target populations, reference standards, and validation endpoints, and the evidentiary boundaries between technical performance, AIS-specific validity, and clinical validity have not been systematically clarified. This review adopted a structured methodological framework and systematically searched the Web of Science Core Collection, PubMed/MEDLINE, and IEEE Xplore, supplemented by forward and backward citation tracking. Study-level evidence was synthesized across imaging inputs, reconstruction tasks, algorithmic paradigms, and validation designs. A total of 115 core studies were included, comprising 32 AIS-specific studies (27.8%), 21 scoliosis-specific or mixed-population studies (18.3%), and 62 studies based on general spinal-image-analysis evidence (53.9%). Based on the reference standards and validation endpoints used, we established a five-level validation framework ranging from image/segmentation-level validation (L1), anatomical-structure-level validation (L2), and clinical-parameter-level validation (L3), to workflow/generalization-level validation (L4) and clinical-translation-level validation (L5). An evidence-gap matrix based on "imaging modality × methodological task" was also constructed. Among the 115 studies, 28 (24.3%) reached a maximum of L1, 49 (42.6%) reached L2, 34 (29.6%) reached L3, and only 4 (3.5%) reached L4; no study met the L5 criteria, and no explicit AIS-specific external validation was identified. Current evidence indicates that CT provides a strong basis for high-fidelity osseous reconstruction, EOS has accumulated relatively continuous AIS-specific evidence for weight-bearing 3D parameter assessment, and 3D ultrasound offers advantages for repeated radiation-free parameter measurement, whereas MRI-based reconstruction and limited-projection 3D recovery remain rapidly evolving fields. The principal challenge has therefore shifted from whether a 3D structure can be generated to whether structural fidelity can be translated into reliable clinical parameters and whether models can generalize consistently across independent AIS populations, imaging systems, and acquisition protocols. Future work should prioritize AIS-specific multicenter validation resources, task-matched 3D reference standards, and a continuous evidence chain linking structural fidelity, clinical-parameter validity, external generalization, and clinical impact, thereby advancing AIS 3D reconstruction from technical feasibility toward trustworthy clinical application.