Artificial Intelligence, Bile Reflux, Tongue
Conditions
Keywords
artificial intelligence, tongue, bile reflux
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Shandong University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
* Patients aged 18 to 80 years who wish to undergo gastroscopy. * Patients have given their informed consent and signed the informed consent form.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | 3 years | Sensitivity of artificial intelligence models Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%. |
| Specificity | 3 years | Specificity of Artificial Intelligence Models Specificity = number of true negatives / (number of true negatives + number of false positives)) \*100% |
| Positive predictive values(PPV) | 3 years | Positive predictive values from artificial intelligence models Positive predictive value = true positive / (true positive + false positive) \*100% |
| Negative predictive values (NPV) | 3 years | Negative predictive values for artificial intelligence models Negative Predictive Value = True Negative / (True Negative + False Negative) \*100% |
| AUC (95% CI) | 3 years | area under the receiver operating characteristic curve (AUC), |
| Accuracy | 3 years | Accuracy for artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects \* 100% |
Countries
China
Contacts
Primary ContactXiuli Zuo, MD, PhD
Outcome results
None listed