EVEREST - IBD: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 4,000
- 试验地点
- 1
- 主要终点
- To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy
研究概览
简要总结
To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy
详细描述
To develop and train a Convolutional Neural Network to detect and characterize disease severity in inflammatory bowel disease during endoscopy. This initiative will inevitably establish a high-quality large image database. Our secondary study aims are therefore to use the images we collect to advance the field of deep learning and computer aided diagnosis in inflammatory bowel disease by establishing an image database. This will involve developing a framework combining deep learning and computer vision algorithms. The ultimate aim is to use the image database to produce high impact research outcomes and training resources leading to an improvement in the quality of endoscopy performed, reduce inter-observer variability in disease assessment and a reduction in missed bowel cancer rates and associated mortality.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 16 Years 至 99 Years（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •• Any adult patient aged 16 years or older who has consented to undergo endoscopic investigation where images are captured as part of routine clinical care.
排除标准
- •• Any patient under the age of 16
- •Patients who are unable to give informed consent to undergo endoscopic investigation or those who do not wish their pseudo-anonymised images to be used
结局指标
主要结局
To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy
时间窗: 5 years
To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy
次要结局
- a) To explore whether Artificial Intelligence can predict response to IBD therapies(5 years)
- b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD.(5 years)
- e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD(5 years)
- f) To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time.(5 years)
- c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy(5 years)
- d) To evaluate comparisons in endoscopic image interpretation between endoscopist's(5 years)