跳至主要内容
临床试验/NCT05110430
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.

Maastricht University1 个研究点 分布在 1 个国家目标入组 2,365 人开始时间: 2021年3月10日最近更新:
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

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

申办方类型
Other
责任方
Sponsor

研究点 (1)

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