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Deep learning-based model construction and application of computer-assisted ultrasound endoscopy to predict the properties and therapeutic risk of gastrointestinal submucosal tumors

Deep learning-based model construction and application of computer-assisted ultrasound endoscopy to predict the properties and therapeutic risk of gastrointestinal submucosal tumors

Status
Recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2100051191
Enrollment
Unknown
Registered
2021-09-15
Start date
2021-09-15
Completion date
Unknown
Last updated
2022-05-23

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

Conditions

Submucosal tumors of the digestive tract

Interventions

perforation&#32
Gold Standard:Pathological diagnosis
Index test:Deep&#32
learn-based&#32
system&#32
for&#32
computer&#32
assisted&#32
ultrasound&#32
endoscopy&#32
to&#32
identify&#32
gastrointestinal&#32
submucosal&#32
tumors,&#32
predict&#32
malignant&#32
potential&#32
and&#32

Sponsors

The Sixth Affiliated Hospital of Sun Yat-Sen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: 1. Pathological diagnosis of submucosal tumor of digestive tract; 2. The image contains a complete tumor; 3. Endoscopic or surgical resection with a complete pathological report.

Exclusion criteria

Exclusion criteria: No.

Design outcomes

Primary

MeasureTime frame
accuracy;sensitivity;specificity;—

Countries

China

Contacts

Public ContactSun Jiachen

The Sixth Affiliated Hospital of Sun Yat-Sen University?

sunjch8@mail.sysu.edu.cn+86 17876767620

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 10, 2026