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LEAF(Liver Tumor dEtection And classiFication AI)

NCT06859840 · Status: ACTIVE_NOT_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 10000

Last updated 2026-05-04

No results posted yet for this study

Summary

This study plans to utilize multiphase contrast-enhanced and non-contrast CT（Computed Tomography） images from 10000 pathologically confirmed liver tumor patients at our hospital. An AI（artificial intelligence） model will be used to outline the 3D contours of liver masses, which will then be refined by radiologists and hepatobiliary-pancreatic surgeons to enhance model accuracy. By incorporating more imaging data, the model's recognition capabilities will be improved, laying the groundwork for prospective clinical trials and aiming to establish a superior AI model for early liver cancer screening based on CT imaging.

Conditions

Interventions

DEVICE

LEAF(Liver tumor dEtection And classiFication AI)

Using the LEAF(Liver tumor dEtection And classiFication AI)model to assist in image interpretation, patients with positive results are recalled for further examination based on the LEAF output information and the original image interpretation, to obtain pathological results and long-term follow-up.

Sponsors & Collaborators

  • Zhejiang University

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-07-15
Primary Completion
2025-10-31
Completion
2030-09-15

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06859840 on ClinicalTrials.gov