A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 3,600
- 试验地点
- 1
- 主要终点
- Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy
研究概览
简要总结
Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model.The deep learning model through the early stage of the study, is able to identify lesions of digest tract.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%. On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination. Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital. It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.
详细描述
Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model。The deep learning model through the early stage of the study, is able to identify lesions of colon polyps, colorectal cancer, colorectal apophysis lesions, colonic diverticulum, ulcerative colitis, gastric ulcer, gastric polyps, submucosal uplift, reflux esophagitis, esophageal ulcer, esophageal polyp, esophageal erosion, esophageal ectopic gastric mucosa and esophagus varicosity, esophageal cancer, esophageal papilloma, etc.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%. On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination. Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital. It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.
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
- •Voluntarily sign the informed consent for this study
- •Stable vital signs
- •Over 18 years old
- •Patients requiring painless gastroenteroscopy for various reasons
排除标准
- •Unable or unwilling to sign a consent form, or unable to follow research procedures
- •have contraindications to painless gastroenteroscopy
- •Vital signs are unstable
- •The lesions have been identified by gastroenteroscopy in other hospitals, which is to further confirm the patients who come to our hospital for endoscopic examination
- •Endoscopic treatment, such as polypectomy, pylorus narrow dilatation and so on
结局指标
主要结局
Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy
时间窗: 2 years
Endoscopic examination has a high dependence on the clinical experience and status of endoscopists, and the quality of endoscopic examination of endoscopists can be reduced by high-load work, and problems such as incomplete examination site coverage, incomplete detection of lesions, and incomplete image collection are easy to occur. Artificial intelligence does not have this weakness. It does not reduce its ability to work over a long period of time, and its assistance is expected to improve the detection rate of lesions
The accuracy of AI-assisted diagnostic model evaluating the intestinal readiness score
时间窗: 2 years
The quality of intestinal preparation determines the quality of colonoscopy, which is evaluated by endoscopists through the Boston score. The ai-assisted diagnostic model can also be automatically graded.The Boston bowel score is used to determine whether the bowel is adequately prepared. The Boston bowel score is divided into 4 grades (0\~3 points) from worst to cleanest. The higher the score is, the better the bowel is prepared and more conducive to colonoscopy.
次要结局
未报告次要终点
