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Construction and AUC optimization verification of a multimodal intelligent diagnosis model for fatty liver based on ultrasound images

Construction and AUC optimization verification of a multimodal intelligent diagnosis model for fatty liver based on ultrasound images

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500106945
Enrollment
Unknown
Registered
2025-07-31
Start date
2025-08-01
Completion date
Unknown
Last updated
2025-08-18

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

Conditions

Fatty liver

Interventions

Gold Standard:ATI test
Index test:Deep learning model

Sponsors

The Seventh Affiliated Hospital Sun Yat-sen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1.(a) 18-80 years old; 2.(b) Those who undergo two-dimensional ultrasound examination and ATI assessment;

Exclusion criteria

Exclusion criteria: 1.(a) Basic information, relevant medical history and test indicators are incomplete; 2.(b) Incomplete sections of the ultrasound image; 3.(c) The image is blurred or the liver parenchyma accounts for less than 50% of the ultrasound image.

Design outcomes

Primary

MeasureTime frame
Receiver Operating Characteristic;Accuracy;Sensitivity;Specificity;—

Countries

China

Contacts

Public Contactxuzuofeng

The Seventh Affiliated Hospital Sun Yat-sen University

xuzuofeng77@aliyun.com+86 755 81206580

Outcome results

None listed

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