Multi-parameter scoliosis evaluation from dual-view x-rays via a local sine-based projection model

OBJECTIVE: To develop a robust and accurate multi-parameter assessment framework for adolescent idiopathic scoliosis (AIS) based on spinal X-ray images, overcoming the limitations of traditional Cobb angle-based evaluation. APPROACH: We proposed a dual-view labeling protocol and employed a Residual U-Net architecture to segment vertebral bodies in 700 paired anteroposterior and lateral X-rays. A novel Local Sine-based Spinal Projection Model (LS-SPM) was introduced to calculate vertebral inclination angles and derive consecutive Cobb angles. Additional spinal parameters, including coronal balance (T1PL-CSVL), force line (FL), and force line balance (FLB), were computed to enable comprehensive spinal evaluation. MAIN RESULTS: The proposed method achieved a vertebral segmentation Dice coefficient of 94.7%. The mean absolute error of the Cobb angle was 2.71°, outperforming the minimum bounding rectangle method (3.46°) and novice manual measurements (5.40°), with an intraclass correlation coefficient of 0.958. In diagnostic grading, 120 out of 142 patients were correctly classified (84.5% success rate). Longitudinal follow-up demonstrated accurate tracking of structural improvements and coronal balance recovery following conservative treatment. SIGNIFICANCE: This approach enables automatic, precise, and comprehensive scoliosis evaluation by integrating fine-grained segmentation with geometric modeling of spinal curvature. The proposed method improves spinal assessment accuracy and facilitates longitudinal scoliosis monitoring, offering substantial clinical utility and impact in computer-aided orthopedic diagnosis.

Authors

Institutions

Publication Details

Journal
Physics in Medicine and Biology
Published
2026-07-17
DOI
https://doi.org/10.1088/1361-6560/ae8ca3
Primary Topic
Scoliosis diagnosis and treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-parameter scoliosis evaluation from dual-view x-rays via a local sine-based projection model

Wenzheng Ding, M Y Wang, Zhenrui Wu, Honglian Xiong et al.
Physics in Medicine and Biology
Scoliosis diagnosis and treatment
article

Multi-parameter scoliosis evaluation from dual-view x-rays via a local sine-based projection model

Wenzheng Ding, M Y Wang, Zhenrui Wu, Honglian Xiong, Guobao Ma, Xianbin Wang
article en

Abstract

OBJECTIVE: To develop a robust and accurate multi-parameter assessment framework for adolescent idiopathic scoliosis (AIS) based on spinal X-ray images, overcoming the limitations of traditional Cobb angle-based evaluation. APPROACH: We proposed a dual-view labeling protocol and employed a Residual U-Net architecture to segment vertebral bodies in 700 paired anteroposterior and lateral X-rays. A novel Local Sine-based Spinal Projection Model (LS-SPM) was introduced to calculate vertebral inclination angles and derive consecutive Cobb angles. Additional spinal parameters, including coronal balance (T1PL-CSVL), force line (FL), and force line balance (FLB), were computed to enable comprehensive spinal evaluation. MAIN RESULTS: The proposed method achieved a vertebral segmentation Dice coefficient of 94.7%. The mean absolute error of the Cobb angle was 2.71°, outperforming the minimum bounding rectangle method (3.46°) and novice manual measurements (5.40°), with an intraclass correlation coefficient of 0.958. In diagnostic grading, 120 out of 142 patients were correctly classified (84.5% success rate). Longitudinal follow-up demonstrated accurate tracking of structural improvements and coronal balance recovery following conservative treatment. SIGNIFICANCE: This approach enables automatic, precise, and comprehensive scoliosis evaluation by integrating fine-grained segmentation with geometric modeling of spinal curvature. The proposed method improves spinal assessment accuracy and facilitates longitudinal scoliosis monitoring, offering substantial clinical utility and impact in computer-aided orthopedic diagnosis.

Physics in Medicine and Biology
Foshan University (CN), First Affiliated Hospital of Heilongjiang University of Chinese Medicine (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 7%
Scoliosis diagnosis and treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.