Analyzing the Link Between Tongue Images and Gastric Cancer Cascade Response Using Artificial Intelligence Techniques
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 4,000
- 试验地点
- 1
- 主要终点
- AUC (95% CI)
研究概览
简要总结
This study combines artificial intelligence with tongue images, by collating and collecting tongue images and diagnostic and pathological results of gastroscopic diseases, mining and analysing the correlation between tongue images and OLGA, OLGIM stages, Correa sequences and constructing prediction models, to deeply investigate the relationship between tongue images and precancerous diseases, precancerous lesions and gastric cancer.
详细描述
Firstly, tongue pictures and patient information will be collected after the patient signed an informed consent form.
Secondly, after the patient undergoes gastroscopy, patient gastroscopy reports and pathology reports will be obtained.
Thirdly, the investigator will assess the patient's gastroscopy report for the Correa sequence of gastric cancer with OLGA and OLGIM staging.
Finally, the patient's tongue image, information and gastric cancer cascade response are matched to construct an artificial intelligence model and assess the quality of the model.
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
- 40 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients between 40 and 80 years of age who are scheduled for gastroscopy.
- •Patients all gave their informed consent and signed the informed consent form.
排除标准
- •Persons with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who are unable to participate in gastroscopy.
- •Patients with previous surgical procedures on the gastrointestinal tract.
- •Patients taking bismuth or other staining drugs.
结局指标
主要结局
AUC (95% CI)
时间窗: 3 years
area under the receiver operating characteristic curve (AUC)
Specificity
时间窗: 3 years
Specificity of Artificial Intelligence Models Specificity = number of true negatives / (number of true negatives + number of false positives))\*100%
Sensitivity
时间窗: 3 years
Sensitivity of artificial intelligence models Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%.
Negative predictive values(NPV)
时间窗: 3 years
Negative predictive values for artificial intelligence models Negative predictive value = true negative / (true negative + false negative)\*100%
Positive predictive values(PPV)
时间窗: 3 years
Positive predictive values from artificial intelligence models Positive predictive value = true positive / (true positive + false positive)\*100%
Accuracy
时间窗: 3 years
Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects \* 100%
次要结局
未报告次要终点
研究者
Xiuli Zuo
doctoral supervisor of Qilu Hospital gastroenterology department
Shandong University