Determination of Carotid Atherosclerotic Plaque Load and Neck Circumference in Cranial CT Angiography With Machine Learning Method and Their Relationship With Each Other
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 300
- 试验地点
- 1
- 主要终点
- Correlation of the machine learning model and manual interpretation
研究概览
简要总结
The aim of this study is to establish a deep learning model to automatically detect the presence and scoring of carotid plaques in neck CTA images, and to determine whether this model is compatible with manual interpretations.
详细描述
Modeling CTA images for carotid artery segments with deep learning method and automatic carotid plaque presence and scoring will be useful and beneficial in clinical practice. The aim of this study is to establish a deep learning model to automatically detect the presence and scoring of carotid plaques in neck CTA images, and to determine whether this model is compatible with manual interpretations.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 90 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •18 years or older
- •Having cranial CTA withdrawn
- •Having blood lipids, HbA1c, blood glucose, AST, ALT measured in 3 months before and 3 months after cranial CTA
排除标准
- •Thyroid disease
- •Having had neck surgery
- •Use of corticosteroids for more than 6 months
- •Presence of lymph nodes in the anterior neck
- •Hypertrophy of neck muscles
结局指标
主要结局
Correlation of the machine learning model and manual interpretation
时间窗: 1 day
Evaluation of the correlation of the presence of plaque in the carotid segments with manual interpretation in the model obtained by machine learning method
次要结局
未报告次要终点
研究者
Elif Yıldırım Ayaz
Principal Investigator
Sultan Abdulhamid Han Training and Research Hospital, Istanbul, Turkey