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Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data

Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data

Recruiting
18 years and older
All
Phase N/A

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Overview

The goal of this clinical trial is to test the performance of neuronal networks trained on ultrasonic raw Data (=radiofrequency data) for the assessment of liver diseases in patients undergoing a clinical ultrasound examination. The general feasibility is currently evaluated in a retrospective cohort.

The main questions the study aims to answer are:

  • Can a neuronal network trained on RF Data perform equally good as elastography in the assessment of diffuse liver diseases?
  • Can a neuronal network trained on RF Data perform better than a neuronal network trained on b-mode images in the assessment of diffuse liver diseases?
  • Can a neuronal network trained on RF Data distinguish focal pathologies in the liver from healthy tissue?

To answer these questions participants with a clinically indicated fibroscan will undergo:

  • a clinical elastography in Case ob suspected diffuse liver disease
  • a reliable ground truth (if normal ultrasound is not sufficient e.g. contrast enhanced ultrasound, biopsy, MRI or CT) in case of focal liver diseases, depending on the standard routine of the participating center
  • a clinical ultrasound examination during which b-mode images and the corresponding RF-Data sets are captured

Eligibility

Inclusion Criteria:

  • scheduled for an ultrasound investigation by an independent physician
  • signed declaration of consent

Exclusion Criteria:

  • smaller interventions in the same liver during the last 2 Week (for example liver biopsy)
  • contrast enhanced ultrasound less than a day ago
  • major intervention at the liver (for example partial resection)

Study details
    Artificial Intelligence
    Ultrasonography
    Elasticity Imaging Techniques
    Liver Diseases
    Metastasis to Liver

NCT06317181

Technische Universität Dresden

23 August 2025

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