Study of bladdeR Cancer Detection in Standard White Light Versus AI-Supported Endoscopy-02
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 64
- 试验地点
- 2
- 主要终点
- Sensitivity of standard WLC compared to WLC assisted by the AI model evaluated with a non-inferiority margin of 5%.
研究概览
简要总结
This study is being conducted to investigate if an artificial intelligence support tool is non-inferior in detecting bladder cancer compared to the traditional method, standard white light cystoscopy (WLC). The researchers will compare how well the artificial intelligence tool and WLC perform in detecting bladder cancer through a controlled, organized testing process.
详细描述
This clinical investigation aims to confirm that an artificial intelligence model utilizing a Convolutional Neural Network (CNN) can achieve sensitivity in detecting bladder cancer that is non-inferior to traditional white light cystoscopy (WLC) in a randomized controlled trial. The investigational artificial intelligence device leverages the advanced capabilities of CNNs, a type of deep learning model designed to analyze visual imagery with high precision.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 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
- 否
入选标准
- •Men and women adults, age >18 years old
- •Suspicion of primary or recurrent bladder cancer
- •Willingness to sign the Informed Consent Form (ICF) for the CI
- •Ability to comprehend the oral and written Patient Information Leaflet (PIL)
排除标准
- •Not able or willing to sign the Informed Consent Form
结局指标
主要结局
Sensitivity of standard WLC compared to WLC assisted by the AI model evaluated with a non-inferiority margin of 5%.
时间窗: 7 month
To determine whether the AI model is non-inferior with regards to sensitivity compared to standard WLC in a randomized controlled trial.
次要结局
未报告次要终点