Prospective pilotstudy for the development and evaluation of softwaretools for non-invasive assessment of fibrosis and steatosis of the liver with machine learning methods
Recruiting
- Conditions
- K76.0Fatty (change of) liver, not elsewhere classified
- Registration Number
- DRKS00022509
- Lead Sponsor
- Goethe-Universität Frankfurt am Main
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting
- Sex
- All
- Target Recruitment
- 80
Inclusion Criteria
Diagnosis of a non-alcoholic fatty liver disease
Exclusion Criteria
- Alcohol consumption = 20 g/d in female and = 30 g/d in male patients
- Decompensated cirrhosis of the liver
- Viral hepatitis
- Non-viral primary or secondary liver diseases other than NAFLD/NASH
- Malignancies (except for basalioma) within the last 5 years, in particular primary or secondary liver tumors
- Orthotopic liver transplantation
- Any comedication known to cause hepatic steatosis (i.e. steroids, Methotrexate, Amiodarone, Tamoxifen, Valproate, Flutamide, etc.)
- Clinically relevant heart insufficiency, cardiac arrhythmias, relevant cardiac valve diseases
- Pregnancy and lactation
Study & Design
- Study Type
- observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method The aim of this pilot study is to establish an algorithm based on ultrasound raw data for the assessment of liver fibrosis and steatosis with methods of machine learning.
- Secondary Outcome Measures
Name Time Method ot applicable.