Connection Between Tongue Signs and Bile Reflux Analysed With Artificial Intelligence

Shandong University1,500 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

1,500

Conditions

Artificial Intelligence Tongue Bile Reflux

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (1)

Qilu hosipital

Jinan, Shandong, China

Outcomes

Primary Outcomes

Sensitivity

Sensitivity of artificial intelligence models Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%.

Time frame: 3 years

Specificity

Specificity of Artificial Intelligence Models Specificity = number of true negatives / (number of true negatives + number of false positives)) \*100%

Time frame: 3 years

Positive predictive values(PPV)

Positive predictive values from artificial intelligence models Positive predictive value = true positive / (true positive + false positive) \*100%

Time frame: 3 years

Negative predictive values (NPV)

Negative predictive values for artificial intelligence models Negative Predictive Value = True Negative / (True Negative + False Negative) \*100%

Time frame: 3 years

AUC (95% CI)

area under the receiver operating characteristic curve (AUC),

Time frame: 3 years

Accuracy

Accuracy for artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects \* 100%

Time frame: 3 years

Central Contacts

Xiuli Zuo, MD, PhD

CONTACT

86 15588818685 zuoxiuli@sdu.edu.cn

Data from ClinicalTrials.gov

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