Research on artificial intelligence development for classification of oral histopathology

JRCT ID: jRCT1060220025

Registered date:04/06/2022

Basic Information

• Recruitment status: Recruiting
• Health condition(s) or Problem(s) studied: Oral squamous cell carcinoma
• Date of first enrollment: 08/11/2021
• Target sample size: 6
• Study type: Observational

Outcome(s)

• Primary Outcome: Accuracy rate by deep learning for the pathological tissue of oral squamous cell carcinoma

Secondary Outcome

Sensitivity / specificity / F1 value / AUC by deep learning for the pathological tissue of oral squamous cell carcinoma
Effect of deep learning on the accuracy of pathological diagnosis of oral squamous cell carcinoma

Key inclusion & exclusion criteria

• Age minimum: >= 20age old
• Age maximum: Not applicable
• Gender: Both

Include 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.

Exclude criteria

1) An unclear section specimen.
2) A section specimen that cannot be used as a virtual slide.

Related Information

• Primary Sponsor: Sukegawa Shintaro