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Creation of an automatic diagnosis system for endoscopic images of esophageal disease using artificial intelligence

Creation of an automatic diagnosis system for endoscopic images of esophageal disease using AI - Creation of an automatic diagnosis system for endoscopic images of esophageal disease using AI

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
JPRN
Registry ID
JPRN-UMIN000039645
Enrollment
5000
Registered
2020-03-30
Start date
2018-12-18
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

Esophageal diseases (such as esophageal malignant tumors, leiomyomas, neuroendocrine tumors, granuloma, reflux esophagitis, and eosinophilic esophagitis) and normal esophagus

Interventions

None listed

Sponsors

Osaka International Cancer Institute Gastrointestinal Oncology
Lead Sponsor
Tada Tomohiro Institute of Gastroenterology and Proctology Nigata University Graduate School of medical and Dental Science Department of Gastroenterology, Fukuoka University hospital AI Medical Service Inc. Department of Gastroenterology and Hepatology, Kumamoto University Hospital Department of Gastroenterology and Hepatology, Kumamoto chuo hospital Department of Gastroenterology and Hepatology,Keio University Hospital Department of Endoscopic Medicine, Mie University Hospital Depart
Collaborator

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Esophageal diseases (such as esophageal malignant tumors, leiomyomas, neuroendocrine tumors, granuloma, reflux esophagitis, and eosinophilic esophagitis) and endoscopic images of normal esophagus (images / movies)

Exclusion criteria

Exclusion criteria: When the patient reject to use existing information through an information disclosure document published on the homepage of the facility.

Design outcomes

Primary

MeasureTime frame
Diagnosis accuracy of various esophageal diseases using AI diagnostic system

Secondary

MeasureTime frame
Diagnosis sensitivity, specificity, positive predictive value, negative predictive value of various esophageal diseases using AI diagnostic system. Time required for diagnosis. Doctor's diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value

Countries

Japan

Contacts

Public ContactKotaro Waki

Osaka International Cancer Institute Gastrointestinal Oncology

waki-ko@mc.pref.osaka.jp06-6945-1181

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026