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

Liver Diseases Clinical Trial

— LivSPECTRUS  

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 Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06317181
• Other study ID #: DifLivSPECTRUS
• Status: Not yet recruiting
• Phase: N/A
• Start date: April 2024
• Est. completion date: December 2025

Study information

• Verified date: March 2024
• Source: Technische Universität Dresden
• Contact: Moritz Herzog, MD
• Phone: 0049 351 458 11501
• Email: moritz.herzog[@]ukdd.de
• Is FDA regulated: No
• Study type: Interventional

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

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 200
• Est. completion date: December 2025
• Est. primary completion date: June 2025
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

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)

Related Conditions & MeSH terms

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

Intervention

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.
• 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

Locations

Country	Name	City	State
n/a

Sponsors (3)

Lead Sponsor	Collaborator
Technische Universität Dresden	University Hospital Dresden, University of Leipzig

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Performance analysis of the trained model	Analysis of the concordance of a Deep Learning-based analysis of RF data with established clinical measures. In case of diffuse disease the stiffness of the tissue and in case of the focal lesions the underlying disease as diagnosed by the local physicians are the measures.; Performance is evaluated by the area under the receiver operating characteristic curve and a correlation coefficient.	After study completion, estimated 1 year

External Links

•   Webpage of the project with updated information, participating partners and resulting publications