Combining Tongue and Gastric Cancer Cascade With Artificial Intelligence

Shandong University4,000 enrolled

Overview

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.

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.

Study Type

OBSERVATIONAL

Enrollment

4,000

Conditions

Artificial Intelligence Tongue Gastric Cancer

Eligibility

Sex: ALLMin age: 40 YearsMax age: 80 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * 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. Exclusion Criteria: * 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.

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 of 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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