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A diagnostic test of an artificial intelligence assisted endoscopic ultrasonographic diagnosis system

A diagnostic test of an deep learning-based assisted endoscopic ultrasonographic diagnosis system with graphical user interface

Status
Recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000039322
Enrollment
Unknown
Registered
2020-10-23
Start date
2020-11-01
Completion date
Unknown
Last updated
2021-01-25

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

Conditions

Gastrointestinal subepithelial lesions(Gastrointestinal submocosal tumors).

Interventions

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Sponsors

Affiliated Hospital of Qingdao University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients diagnosed with submucosal tumors by ordinary white light endoscopy and ultrasound endoscopy.

Exclusion criteria

Exclusion criteria: Nil

Design outcomes

Primary

MeasureTime frame
Diagnostic accuracy;AUC;

Secondary

MeasureTime frame
specificity;sensitivity;positive predictive value;negative predictive value;

Countries

China

Contacts

Public ContactLi Xiaoyu

The Affiliated Hospital of Qingdao University

lixiaoyu05@163.com+86 17853299218

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026