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Development and validation of deep learning model for quantitative analysis of liver fibrosis

Development and validation of a deep learning system for quantification of liver fibrosis using contrast agent–enhanced CT images in the liver

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000039703
Enrollment
Unknown
Registered
2020-11-06
Start date
2020-11-01
Completion date
Unknown
Last updated
2021-02-16

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

Conditions

liver fibrosis

Interventions

Gold Standard:Paraffin pathological diagnosis results
Index test:Medical&#32
artificial&#32
intelligence&#32
technology&#32

Sponsors

Renji Hospital Affiliated to Medical College of Shanghai Jiaotong University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Patients over 18 years old; 2. Patients with pathological reports including liver fibrosis stage; 3. Patients without history of liver surgery; 4. Those patients without liver tumors larger than 10 cm in diameter; 5. Patients who underwent CT examination within 3 months after liver pathological evaluation.

Exclusion criteria

Exclusion criteria: 1. Patients with incomplete clinical data; 2. Patients who could not get CT images; 3. Patients whose liver tissue is insufficient for fibrosis staging.

Design outcomes

Primary

MeasureTime frame
Deep learning features based on CT images;Pathological diagnosis of paraffin;collagen proportionate area;—

Countries

China

Contacts

Public ContactFeng Xue

Renji Hospital Affiliated to Medical College of Shanghai Jiaotong University

fengxue6879@163.com+86 13701813929

Outcome results

None listed

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