Research of AI diagnosis in clinical cytology

Not Applicable

• Conditions: cancer; cytology, AI, diagnosis; D003581

• Registration Number: JPRN-jRCT1040230027
• Lead Sponsor: Ikeda Katsuhide

Brief Summary

Not available

Detailed Description

Not available

Study Timeline

• Results First Posted: 1900-01-01
• Study Start: 2023-05-26
• Last Update Posted: 17 October 2023

Recruitment & Eligibility

• Status: Pending
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

Cases in which a cytological examination has been performed and a histologic diagnosis has been made.
effusion sample (malignant mesothelioma, adenocarcinoma, malignant lymphoma, mesothelial cells, etc)
urine sample (urothelial carcinoma, urothelial cells, etc)
respiratory sample (squamous cell carcinoma, adenocarcinoma, small cell carcinoma, squamous cells, etc)
lymph node sample (malignant lymphoma, metastatic carcinoma, etc)
uterine cervix sample (intraepithelial lesion, squamous cell carcinoma, adenocarcinoma, etc)

Exclusion Criteria

Cases in which a histologic diagnosis has not been made.
Rare cases.

Study & Design

• Study Type: Observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Detection rate and Classification rate using the created deep learning model.