Artificial Intelligence, Elasticity Imaging Techniques, Liver Diseases, Metastasis to Liver, Ultrasonography
Conditions
Keywords
Quantitative Ultrasound, Radiofrequency Data
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 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
Interventions
DEVICECollection 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.
DEVICECollection 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
University Hospital Dresden
University of Leipzig
Technische Universität Dresden
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
NONE
Intervention model description
Data is collected in a defined group of patients (all patients listed for elastography or for ultrasonic investigation due to a focal lesion in participating departments). Afterwards performances of algorithms are compared based on this group.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Performance analysis of the trained model | After study completion, estimated 1 year | 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. |
Countries
Germany
Contacts
Primary ContactMoritz Herzog, MD
Outcome results
None listed