Training Physicians to Differentiate the Paris Classification Using Artificial Colon Polyp Images
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 70
- 试验地点
- 1
- 主要终点
- Accuracy for Paris classification
研究概览
简要总结
Training in endoscopy is essential for the early detection of precursors of colorectal cancer. Up to now, this training has been carried out with image collections of findings and in practice when working on patients. The investigators want to use artificial intelligence (AI) to better train doctors to recognise these precursors. By using generative AI, the investigators were able to create realistic images that comply with data protection regulations and whose content can be predefined. Parts of the image can also be regenerated so that it is possible to create different precancerous stages in the same place in the image.
In this study the investigators want to train physicians using real images or artificial images in order to compare which version helps classify polyps better.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Basic Science
- 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
- •Physicians with or without experience in colonoscopy
排除标准
- 未提供
结局指标
主要结局
Accuracy for Paris classification
时间窗: 9 months
Ability to correctly classify colonic polyps using the Paris classification
次要结局
- Influence regular usage of Paris classification on accuracy for correct Paris classification(9 months)
- Range of misclassifications for Paris classification(9 months)
- Influence of endoscopy experience on accuracy for correct Paris classification(9 months)
- Influence of time to complete course on accuracy for correct Paris classification(9 months)