NCT05110430已完成不适用
In Silico Clinical Trial Comparing the Reading Accuracy of Doctors and a Deep Learning Algorithm for Detection of Metastatic Bone Disease on Bone Scintigraphy Scans.
适应症
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 2,365
- 试验地点
- 1
- 主要终点
- The classification performance of DL algorithm compared to the ground truth
研究概览
简要总结
Bone scintigraphy scans are two dimensional medical images that are used heavily in nuclear medicine. The scans detect changes in bone metabolism with high sensitivity, yet it lacks the specificity to underlying causes. Therefore, further imaging would be required to confirm the underlying cause. The aim of this study is to investigate whether deep learning can improve clinical decision based on bone scintigraphy scans.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients who underwent a bone scintigraphy scan that is available with the radiologic report between 2010-2018
排除标准
- •The lack of a bone scan, or corresponding radiologic report
结局指标
主要结局
The classification performance of DL algorithm compared to the ground truth
时间窗: June 2021
Reporting the performance measures (Area under the curve, accuracy, specificity..etc)
次要结局
- Comparing the classification performance of the DL algorithm to that of physicians(June 2021)
研究者
研究点 (1)
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