Combining Tongue and Gastric Cancer Cascade With Artificial Intelligence

June 20, 2022 updated by: Xiuli Zuo, Shandong University

Analyzing the Link Between Tongue Images and Gastric Cancer Cascade Response Using Artificial Intelligence Techniques

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.

Study Overview

Status

Not yet recruiting

Conditions

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 Type

Observational

Enrollment (Anticipated)

4000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Shandong
      • Jinan, Shandong, China, 250012
        • Qilu hosipital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

40 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients aged 40-80 years who will undergo gastroscopy and fulfil the inclusion criteria who do not meet the exclusion criteria.

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity
Time Frame: 3 years
Sensitivity of artificial intelligence models Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%.
3 years
Specificity
Time Frame: 3 years
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)
Time Frame: 3 years
Positive predictive values from artificial intelligence models Positive predictive value = true positive / (true positive + false positive)*100%
3 years
Negative predictive values(NPV)
Time Frame: 3 years
Negative predictive values for artificial intelligence models Negative predictive value = true negative / (true negative + false negative)*100%
3 years
AUC (95% CI)
Time Frame: 3 years
area under the receiver operating characteristic curve (AUC)
3 years
Accuracy
Time Frame: 3 years
Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects * 100%
3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shandong University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

June 30, 2022

Primary Completion (Anticipated)

June 30, 2024

Study Completion (Anticipated)

June 30, 2025

Study Registration Dates

First Submitted

May 5, 2022

First Submitted That Met QC Criteria

May 5, 2022

First Posted (Actual)

May 10, 2022

Study Record Updates

Last Update Posted (Actual)

June 22, 2022

Last Update Submitted That Met QC Criteria

June 20, 2022

Last Verified

June 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2022SDU-QILU-G001

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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