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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Bile Reflux Group | Gastroscopic reports of enrolled patients will be extracted and patients will be identified as having bile reflux according to Kellosalo J classification. Grade I: small amount of yellowish reflux emerging from the pyloric orifice and/or yellowish staining of the mucus lake, which is pale yellow in colour. Grade II: intermittent gush of reflux from the pyloric opening and/or yellowish staining of the mucus lake, which is dark yellow. Grade III: frequent gush of yellow-green reflux from the pyloric orifice and/or yellow-green mucus covering the stomach. | ||
| Non-biliary reflux group | Gastroscopic reports will be extracted from patients enrolled in the group that do not meet the Kellosalo J classification as the non-biliary reflux group. |
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| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity | Sensitivity of artificial intelligence models Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%. | 3 years |
| Specificity | Specificity of Artificial Intelligence Models Specificity = number of true negatives / (number of true negatives + number of false positives)) *100% | 3 years |
| Positive predictive values(PPV) | Positive predictive values from artificial intelligence models Positive predictive value = true positive / (true positive + false positive) *100% | 3 years |
| Negative predictive values (NPV) | Negative predictive values for artificial intelligence models Negative Predictive Value = True Negative / (True Negative + False Negative) *100% | 3 years |
| AUC (95% CI) | area under the receiver operating characteristic curve (AUC), | 3 years |
| Accuracy | Accuracy for artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects * 100% | 3 years |
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Inclusion Criteria:
Exclusion Criteria:
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Patients aged 18-80 years who will undergo gastroscopy and who fulfil the inclusion criteria and do not fulfil the exclusion criteria.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Xiuli Zuo, MD, PhD | Contact | 86 15588818685 | 0531-88369277 | zuoxiuli@sdu.edu.cn |
| Name | Affiliation | Role |
|---|---|---|
| Xiuli Zuo, MD,PhD | Qilu Hospital of Shandong University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Qilu hosipital | Jinan | Shandong | 250012 | China |
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| ID | Term |
|---|---|
| D001655 | Bile Reflux |
| ID | Term |
|---|---|
| D001660 | Biliary Tract Diseases |
| D004066 | Digestive System Diseases |
| D004383 | Duodenogastric Reflux |
| D013272 | Stomach Diseases |
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| D005767 | Gastrointestinal Diseases |