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

Liver Diseases Clinical Trial

Official title:
Acquisition and Frequency Spectroscopic Evaluation of Broadband Clinical Ultrasound Raw Data for Liver Cirrhosis and Focal Pathologies Using Neural Networks for Tissue and Pathology Differentiation

Clinical Trial 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

Clinical Trial Description

n/a

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT06317181
Study type Interventional
Source Technische Universität Dresden
Contact Moritz Herzog, MD
Phone 0049 351 458 11501
Email moritz.herzog@ukdd.de
Status Not yet recruiting
Phase N/A
Start date April 2024
Completion date December 2025
View Details
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