Acquisition and Frequency Spectroscopic Evaluation of Broadband Clinical Ultrasound Raw Data for Liver Cirrhosis and Focal Pathologies Using Neural Networks for Tissue and Pathology Differentiation
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 200
- 试验地点
- 6
- 主要终点
- Performance analysis of the trained model
研究概览
简要总结
The goal of this clinical trial is to test the performance of neuronal networks trained on ultrasonic raw Data (=radiofrequency data) for the assessment of liver diseases in patients undergoing a clinical ultrasound examination. The general feasibility is currently evaluated in a retrospective cohort.
The main questions the study aims to answer are:
- Can a neuronal network trained on RF Data perform equally good as elastography in the assessment of diffuse liver diseases?
- Can a neuronal network trained on RF Data perform better than a neuronal network trained on b-mode images in the assessment of diffuse liver diseases?
- Can a neuronal network trained on RF Data distinguish focal pathologies in the liver from healthy tissue?
To answer these questions participants with a clinically indicated fibroscan will undergo:
- a clinical elastography in Case ob suspected diffuse liver disease
- a reliable ground truth (if normal ultrasound is not sufficient e.g. contrast enhanced ultrasound, biopsy, MRI or CT) in case of focal liver diseases, depending on the standard routine of the participating center
- a clinical ultrasound examination during which b-mode images and the corresponding RF-Data sets are captured
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •scheduled for an ultrasound investigation by an independent physician
- •signed declaration of consent
排除标准
- •smaller interventions in the same liver during the last 2 Week (for example liver biopsy)
- •contrast enhanced ultrasound less than a day ago
- •major intervention at the liver (for example partial resection)
结局指标
主要结局
Performance analysis of the trained model
时间窗: After study completion, estimated 1 year
Analysis of the concordance of a Deep Learning-based analysis of RF data with established clinical measures. In case of diffuse disease the stiffness of the tissue and in case of the focal lesions the underlying disease as diagnosed by the local physicians are the measures. Performance is evaluated by the area under the receiver operating characteristic curve and a correlation coefficient.
次要结局
未报告次要终点
研究者
Moritz Herzog
Principal Investigator
Technische Universität Dresden