Development of artificial intelligence (AI) for Diagnosis of Endoscopic images
Not Applicable
Completed
- Conditions
- gastrointestinal disease, esophageal cancer and inflammatory disease.
- Registration Number
- JPRN-jRCT1090220283
- Lead Sponsor
- Tomohiro Tada
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 100000
Inclusion Criteria
patient who areee with usage of endscopic images for this study
Exclusion Criteria
patient who does not areee with usage of endscopic images for this study
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method -Detection rate and detection speed of stomach cancer and esophageal cancer<br>- Percentage of correct diagnosis in distinguishing benign or malignant tumor<br>-Correct diagnosis rate in differentiation of inflammatory bowel disease
- Secondary Outcome Measures
Name Time Method -Differentiation of presence or absence of Helicobacter pylori infection from gastritis images<br>-Ability to pick up lesions with high cancer risk<br>-Diagnostic ability for ulcerative colitis<br>-Diagnosis of cancer depth<br>-Examination of the possibility of secondary image interpretation support in gastric cancer screening<br>-Examination of gastrointestinal lesions using magnifying and ultra-magnifying endoscopes<br>-Examination of whether it is possible to diagnose small intestine and large intestine lesions using a capsule endoscope