Research on artificial intelligence development for classification of oral histopathology
Not Applicable
Recruiting
- Conditions
- Oral squamous cell carcinoma
- Registration Number
- JPRN-jRCT1060220025
- Lead Sponsor
- Sukegawa Shintaro
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting
- Sex
- All
- Target Recruitment
- 6
Inclusion Criteria
1) The pathological histology is diagnosed by a pathologist and can be used as a virtual slide.
2) A histopathological diagnosis made for a patient with squamous cell carcinoma of the oral cavity.
3) The age is 20 years or older.
Exclusion Criteria
1) An unclear section specimen.
2) A section specimen that cannot be used as a virtual slide.
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Accuracy rate by deep learning for the pathological tissue of oral squamous cell carcinoma
- Secondary Outcome Measures
Name Time Method Sensitivity / specificity / F1 value / AUC by deep learning for the pathological tissue of oral squamous cell carcinoma<br>Effect of deep learning on the accuracy of pathological diagnosis of oral squamous cell carcinoma