Artificial Intelligence-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis to Achieve Gastric Cancer Risk Assessment
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 1,500
- 试验地点
- 1
- 主要终点
- Accuracy of AI model to diagnose the Kimura-Takemoto classification
研究概览
简要总结
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification.
详细描述
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. The higher the score, the more severe the degree of atrophic gastritis. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification of atrophic gastritis to achieve gastric cancer risk assessment.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients aged 18-80 years who undergo the white light endoscope examination Informed consent form provided by the patient.
排除标准
- •patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric;
- •disorders who cannot participate in gastroscopy;
- •Patients with progressive gastric cancer;
- •low quality pictures;
- •patients with previous surgical procedures on the stomach or esophageal;
- •patients who refuse to sign the informed consent form;
结局指标
主要结局
Accuracy of AI model to diagnose the Kimura-Takemoto classification
时间窗: 2 years
Accuracy of AI model to diagnose the Kimura-Takemoto classification
Specificity of AI model to diagnose the Kimura-Takemoto classification
时间窗: 2 years
Specificity of AI model to diagnose the Kimura-Takemoto classification
Sensitivity of AI model to diagnose the Kimura-Takemoto classification
时间窗: 2 years
Sensitivity of AI model to diagnose the Kimura-Takemoto classification
次要结局
- The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture(2 years)
研究者
Yanqing Li
Vice President of Qilu Hospital
Shandong University