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

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

NCT Number	NCT06317181
Lead Sponsor	Technische Universität Dresden Collaborators: University Hospital Dresden, University of Leipzig
Conditions	Artificial Intelligence, Ultrasonography, Elasticity Imaging Techniques, Liver Diseases, Metastasis to Liver
Enrollment	200 participants
Start Date	2024-04-01
Primary Completion	2025-10 (estimated)
Study Completion	2025-12 (estimated)
Updated on ClinicalTrials.gov	2025-08-20