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

Summary

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

Conditions

• Artificial Intelligence
• Ultrasonography
• Elasticity Imaging Techniques
• Liver Diseases
• Metastasis to Liver

Interventions

DEVICE

Collection of elastography data

patients who are scheduled for an elastography for clinical reasons usually receive an ultrasound scan in which the b-mode images of the liver tissue are collected. In this study additional radiofrequency data is collected through a software access.

DEVICE

Collection of ultrasonic raw data

Patients who are transferred to the ultrasound departement due to suspicious focal lesions receive an ultrasonic investigation including the acquisition of raw data and extracting a definitive diagnose from the following clinical routine investigation, depending on the standards of the participating center

Sponsors & Collaborators

• University Hospital Dresden

  collaborator OTHER

• University of Leipzig

  collaborator OTHER

• Technische Universität Dresden

  lead OTHER

Principal Investigators

• Moritz Herzog, MD · University Hospital Dresden

Study Design

Allocation

NON_RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2024-04-01

Primary Completion

2025-10-31

Completion

2025-12-31

Countries

• Germany

Study Locations