Combining Tongue and Gastric Cancer Cascade With Artificial Intelligence

Not yet recruiting

• Conditions: Artificial Intelligence; Tongue; Gastric Cancer

• Registration Number: NCT05368636
• Lead Sponsor: Shandong University

Brief Summary

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.

Detailed Description

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 Timeline

• Study First Submitted: 2022-05-05
• Study First Submitted QC: 2022-05-05
• Study First Posted: 2022-05-10
• Status Verified: 2022-06
• Last Update Submitted: 2022-06-20
• Last Update Posted: 2022-06-22
• Study Start: 2022-06-30
• Primary Completion: 2024-06-30
• Study Completion: 2025-06-30

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 4000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
AUC (95% CI)	3 years	area under the receiver operating characteristic curve (AUC)
Specificity	3 years	Specificity of Artificial Intelligence Models Specificity = number of true negatives / (number of true negatives + number of false positives))\*100%
Sensitivity	3 years	Sensitivity of artificial intelligence models Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%.
Negative predictive values(NPV)	3 years	Negative predictive values for artificial intelligence models Negative predictive value = true negative / (true negative + false negative)\*100%
Positive predictive values(PPV)	3 years	Positive predictive values from artificial intelligence models Positive predictive value = true positive / (true positive + false positive)\*100%
Accuracy	3 years	Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects \* 100%

Trial Locations

Locations (1)

Qilu hosipital; 🇨🇳 Jinan, Shandong, China