Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 200
- 主要终点
- Detection rate of pulmonary conditions
研究概览
简要总结
The main objective of the study is to evaluate the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with innovative vendor-neutral CT denoising solution based on deep learning technology.
详细描述
Considering lung cancer-related public health challenges, a reliable lung cancer screening method for high-risk cohorts in Mongolia is needed. Thus, our study aims to assess the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with artificial intelligence based CT denoising technique among various patient groups.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients aged over 18-year-old
- •Patients undergoing CT Chest for all purpose
排除标准
- •Age less than 18 years
- •Any suspicion of pregnancy
- •History of thoracic surgery or placement of the metallic device in the thorax
- •An inability to hold respiration during CT
结局指标
主要结局
Detection rate of pulmonary conditions
时间窗: Within 2 weeks after data collection
Pulmonary condition detection rate on low dose chest CT and ultra dose chest CT with artificial intelligence-based CT denoising solution by blinded reviewers
Contrast media dose
时间窗: Within 2 weeks after data collection
Administered contrast media dose in each patient
次要结局
- Image contrast(Within 2 weeks after data collection)
研究者
Bayarbaatar Bold
Principal Investigator, Bayarbaatar Bold, Diagnostic Radiologist, M.D, Intermed Hospital
Intermed Hospital