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Interventional AI-Human Collaboration for Liver Tumor Diagnosis

NCT07153783 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 10333

Last updated 2025-11-18

No results posted yet for this study

Summary

Recent advances in artificial intelligence (AI), particularly deep learning technology, have transformed medical imaging analysis. AI systems have demonstrated diagnostic performance comparable to or exceeding that of expert radiologists in specific tasks. Liver-focused AI diagnostic systems have achieved promising results in multi-center validations; however, these retrospective studies have not yet addressed two critical gaps. First, large-scale prospective trials are required to establish real-world clinical effectiveness. Second, it remains unclear whether AI can be organically embedded into clinical diagnostic workflows to reshape diagnostic and therapeutic pathways, particularly by enhancing the detection and follow-up of hepatic malignancies and ultimately improving patient outcomes.

Conditions

  • Hepatocellular Carcinoma (HCC)
  • Intrahepatic Cholangiocarcinoma (Icc)
  • Hepatic Metastasis
  • Hepatic Hemangioma
  • Cyst
  • Focal Nodular Hyperplasia

Interventions

DIAGNOSTIC_TEST

AI-human collaboration for CE-CTs diagnosis

The system automatically processes all eligible same-day scans and generates results for review the following day. To maintain efficient AI-human collaboration while preserving the standard clinical workflow, the conventional radiological interpretation process remains unchanged (first-line radiologists provide initial reports followed by senior radiologists' review). A dedicated senior radiologist then evaluates any discordances between AI findings and primary radiological report. For complex cases, the review process escalates to a consensus review panel (i.e., pre-designated senior radiologists, Multidisciplinary Team (MDT)). The MDT can recommend clinical interventions including follow-up (e.g., additional imaging examinations, active surveillance), surgical procedures, or adjustments to adjuvant therapy (initiation or modification of treatment regimens). All discordant cases and their outcomes are systematically documented for longitudinal tracking and follow-up analysis.

Sponsors & Collaborators

  • Shengjing Hospital

    lead OTHER

Principal Investigators

  • Yu Shi, MD PhD · Shengjing Hospital

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-09-01
Primary Completion
2025-10-29
Completion
2025-11-07

Countries

  • China

Study Locations

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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 NCT07153783 on ClinicalTrials.gov