Research of AI diagnosis in clinical cytology

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-jRCT1040230027

Enrollment

1000

Registered

2023-05-26

Start date

2023-05-26

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

cancer cytology, AI, diagnosis

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

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

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

Design outcomes

Primary

Measure	Time frame
Detection rate and Classification rate using the created deep learning model.	—

Contacts

Public ContactKatsuhide Ikeda

NAGOYA University

k-ikeda@met.nagoya-u.ac.jp+81-52-719-3152

Outcome results