Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions
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
- The accuracy rate of diagnosing adenomas
研究概览
简要总结
This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
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
- •Patients aged ≥ 18 years, who need to undergo colonoscopy, regardless of gender.
- •Voluntarily sign the informed consent form
- •Promise to abide by the research procedures and cooperate in the implementation of the entire research process.
排除标准
- •Patients who has a history of abdominal or pelvic surgery or radiotherapy in the past;
- •Patients who has definite active lower gastrointestinal bleeding.
- •Existing or suspected hereditary colorectal polyposis, inflammatory bowel disease;
- •Uncontrolled hypertension (systolic blood pressure > 160 mmHg or diastolic blood pressure > 95 mmHg after standardized treatment)
- •There is a history of stroke, coronary artery disease, or vascular disease;
- •Pregnant;
- •Intestinal preparation cannot be carried out.
研究组 & 干预措施
Traditional colonoscopy examination group
the system shows the original colonoscopy video.
AI-assisted colonoscopy examination group
The system will present the detected polyp positions as hollow blue and set an alarm box directly on the high-definition monitor to mark whether it is a polyp. Hollow red is used to set an alarm box directly on the high-definition monitor to mark whether it is an adenoma.
干预措施: AI models with NBI (Device)
结局指标
主要结局
The accuracy rate of diagnosing adenomas
时间窗: during endoscopy
The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.
次要结局
- The prediction for the disease risk level(during endoscopy)
研究者
ChenMingkai
Professor
Wuhan University