Deep-learning Based Classification of Spine CT
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- segmentation accuracy
研究概览
简要总结
It is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.
详细描述
Computer tomography (CT) is one of the most important imaging tool to assist the diagnostic and treatment of spinal disease. Classification of specific targets (e.g. individuals, lesions, etc.) is one of the most common mission of medical image analysis. However, it is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •spinal thin layer CT
- •Exclusion Critera:
- •medals or other implants induce artifact
- •poor image quality
排除标准
- 未提供
结局指标
主要结局
segmentation accuracy
时间窗: 1 day
segmentation accuracy of multiple structures (e.g. Dice score, etc.)
classification accuracy
时间窗: 1 day
classification accuracy (e.g. area under the curve, etc.)
次要结局
未报告次要终点
研究者
Shisheng He, MD
Executive Director of Orthopedic Department
Shanghai 10th People's Hospital