Construction of artificial intelligence-based prediction models for non-recognizable thoracolumbar compression fractures by X-ray inspection
10.3969/j.issn.1002-1671.2024.04.024
- VernacularTitle:对于X线难以鉴别的胸腰椎压缩骨折的人工智能预测模型的构建
- Author:
Yi LIU
1
,
2
;
Jianhua CUI
;
Sibin LIU
Author Information
1. 长江大学附属荆州医院影像科,湖北 荆州 434000
2. 威海卫人民医院影像科,山东 威海 264200
- Keywords:
vertebral compression fractures;
radiography;
artificial intelligence
- From:
Journal of Practical Radiology
2024;40(4):617-620
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the potency of applying an artificial intelligence(AI)based model for classifying vertebral fractures in lumbar X-ray images.Methods Patients who underwent lateral lumbar X-ray and MRI were retrospectively selected.Based on MRI results,the vertebrae were categorized as fresh fractures,old fractures,and normal vertebrae.A ResNet-18 classification model was constructed using delineated region of interest(ROI)on the X-ray images,and the model's performance was evaluated.Results A total of 272 patients(662 vertebrae)were included in this study.The vertebrae were randomly divided into training(n=529)and validation(n=133)sets.The model's performance in discerning normal vertebrae,fresh fractures,and old fractures revealed accuracy of 0.91,0.42,and 0.75,and the sensitivity were 0.91,0.408,and 0.72,while the specificity were 0.796,0.892,and 0.796,respectively.Conclusion The X-ray-based ResNet-18 AI model has significant accuracy for distinguishing old fractures and normal vertebrae;However,the model's accuracy needs further improvement for distinguishing fresh fractures.
