Prediction and Clinical Evaluation of Cobb Angle in Idiopathic Scoliosis Using Machine Learning and Three-Point Mechanical Data of 3D-Printed Orthotics
10.16156/j.1004-7220.2025.02.016
- VernacularTitle:基于3D打印矫形器三点力学数据与机器学习的特发性脊柱侧弯Cobb角预测及临床评价
- Author:
Xunjun MA
1
;
Ya LI
;
Jun YU
;
Haitao LIU
;
Yuncheng WU
;
Jinwu WANG
Author Information
1. 徐州医科大学 医学信息与工程学院,江苏 徐州 221004
- Publication Type:Journal Article
- Keywords:
idiopathic scoliosis;
three-dimensional(3D)-printed orthotics;
Cobb angle prediction;
machine learning;
radiation-free assessment
- From:
Journal of Medical Biomechanics
2025;40(2):364-370
- CountryChina
- Language:Chinese
-
Abstract:
Objective A Cobb angle prediction model for adolescent idiopathic scoliosis(AIS)based on three-point mechanical data from three-dimensional(3D)-printed orthotics and various machine learning algorithms was developed,so as to provide an innovative,radiation-free method for early clinical screening and monitoring of AIS.Methods Clinical data from AIS patients and mechanical data from 3D-printed orthotics were collected to construct a comprehensive dataset with features such as gender,age,disease type,weight,and Risser score.Six algorithms,namely,random forest,support vector regression,gradient boosting regressor,extreme gradient boosting,lightgbm,and catboost,were used to construct and evaluate the performance of Cobb angle prediction models.Results The gradient boosting regressor model had the best performance on several evaluation metrics,achieving a precision rate of 0.937,recall rate of 0.818,F1-score of 0.949,and an area under curve(AUC)value of 0.843.In the validation set,the model's predictions reached an accuracy rate of 0.942,fitting well with the actual Cobb values.Conclusions The Cobb angle prediction model based on mechanical data and machine learning effectively avoids the radiation risks associated with traditional full-spine X-ray examinations in early clinical screening.It provides a non-invasive assessment for AIS patients,enhancing the safety and efficiency of screening and monitoring,and offering a powerful decision-making tool for clinicians,with a great clinical significance.
