Ignite Creation Date:
2024-05-06 @ 8:16 PM
Last Modification Date:
2024-10-26 @ 3:24 PM
Study NCT ID:
NCT06317181
Status:
RECRUITING
Last Update Posted:
2024-03-19
First Post:
2024-03-01
Brief Title:
Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data
Sponsor:
Technische Universität Dresden
Organization:
Technische Universität Dresden
Study Overview
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
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
LivSPECTRUS
Brief 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 eg 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
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 eg 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
True
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
None