Analysing the Link Between Tongue Signs and Bile Reflux by Artificial Intelligence
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 1,500
- 试验地点
- 1
- 主要终点
- Sensitivity
研究概览
简要总结
By introducing artificial intelligence into Chinese medicine tongue diagnosis, we collated and collected tongue images, anxiety and depression scales and gastroscopy reports, mined and analysed the correlation between tongue images and bile reflux and anxiety and depression and constructed a prediction model to analyse the possibility of predicting bile reflux and anxiety and depression in patients based on tongue images.
详细描述
Firstly, after the patient signs the informed consent form, the researcher will collect pictures of the patient's tongue and obtain basic information about the patient.
Second, the patients are scored on the Anxiety and Depression Scale.
Thirdly, after the patient undergoes gastroscopy, the patient's gastroscopy report is obtained.
Finally, the patient's tongue image, information and gastroscopy report are matched to construct an artificial intelligence model of tongue image and bile reflux and anxiety and depression, and the quality of the model is assessed.
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 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients aged 18 to 80 years who wish to undergo gastroscopy.
- •Patients have given their informed consent and signed the informed consent form.
排除标准
- •Serious heart, liver, kidney or other underlying illness, or mental illness.
- •Patients taking anti-anxiety or depression medication within 3 months.
- •Current H. pylori infection.
- •History of surgery on the digestive or biliary tract.
- •Peptic ulcer, malignant tumour of the digestive tract, etc.
- •Patients taking bismuth or other staining medications.
- •Pregnant or lactating women.
结局指标
主要结局
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%.
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%
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%
AUC (95% CI)
时间窗: 3 years
area under the receiver operating characteristic curve (AUC),
Accuracy
时间窗: 3 years
Accuracy for 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