Application of Deep Learning to Diagnose and Classify Adolescent Idiopathic Scoliosis
10.12455/j.issn.1671-7104.230700
- VernacularTitle:深度学习在诊断和分类青少年特发性脊柱侧凸中的应用研究
- Author:
Kunjie XIE
1
;
Wei LEI
;
Suping ZHU
;
Yaopeng CHEN
;
Jincong LIN
;
Yi LI
;
Yabo YAN
Author Information
1. 空军军医大学西京医院骨科,西安市,710032
- Keywords:
adolescent idiopathic scoliosis;
deep learning;
X-ray images;
Cobb angel;
diagnosis
- From:
Chinese Journal of Medical Instrumentation
2024;48(2):126-131
- CountryChina
- Language:Chinese
-
Abstract:
A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed.This model mainly included key points detection and Cobb angle measurement.748 full-length standing spinal X-ray images were retrospectively collected,of which 602 images were used to train and validate the model,and 146 images were used to test the model performance.The results showed that the model had good diagnostic and classification performance,with an accuracy of 94.5%.Compared with experts'measurement,94.9%of its Cobb angle measurement results were within the clinically acceptable range.The average absolute difference was 2.1°,and the consistency was also excellent(r2≥0.9552,P<0.001).In the future,this model could be applied clinically to improve doctors'diagnostic efficiency.
