Evaluation of a Novel Auto Segmentation Algorithm for Normal Structure Delineation in Radiation Treatment Planning
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- Mayo Clinic
- 入组人数
- 200
- 试验地点
- 14
- 主要终点
- Proportion of success
研究概览
简要总结
This study measures the utility of a novel artificial intelligence (AI) algorithm for performing auto-segmentation of computed tomography (CT) scans for radiation therapy planning.
详细描述
PRIMARY OBJECTIVE:
I. To measure the observed utility of an AI algorithm for normal segmentation by recording study subjects' observations of its function.
OUTLINE: This is an observational study.
Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Employment at Mayo Clinic Arizona, Florida, or Rochester (which includes Regional Practice sites located at Mayo Clinic Health System locations) as train clinical staff that participate in normal tissue segmentation
排除标准
- •Inability to complete study surveys
研究组 & 干预措施
Observational
Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study.
干预措施: Non-Interventional Study (Other)
结局指标
主要结局
Proportion of success
时间窗: Baseline
Will be evaluated by question 1 of the end user survey, which evaluates the level of modification to the artificial intelligence generated auto-segmentation structures that was required (no modification, minor modification, or major modification). Auto-segmentation algorithm data will be collected through an electronic data collection form.
次要结局
未报告次要终点