Research on artificial intelligence development for classification of oral histopathology

Research on artificial intelligence development for automatic classification of oral histopathology

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-jRCT1060220025

Enrollment

Registered

2022-06-04

Start date

2021-11-08

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Oral squamous cell carcinoma

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

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

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

Design outcomes

Primary

Measure	Time frame
Accuracy rate by deep learning for the pathological tissue of oral squamous cell carcinoma	—

Secondary

Measure	Time frame
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	—

Contacts

Public ContactShintaro Sukegawa

Kagawa Prefectural Central Hospital

gouwan19@gmail.com+81-87-811-3333

Outcome results