Using deep learning to assess liver diseases with ultrasound data

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This clinical trial aims to evaluate the effectiveness of neural networks trained on raw ultrasound radiofrequency data for assessing liver diseases in patients undergoing ultrasound examinations. The study will compare the performance of these neural networks against traditional elastography methods and other neural networks trained on standard b-mode images. Participants will undergo clinical elastography and additional ultrasound assessments to establish a reliable ground truth for the evaluation. The goal is to determine if the deep learning approach can accurately identify diffuse liver diseases and distinguish focal liver pathologies from healthy tissue.

Why it matters

Who can join: the full rules

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)

Where it's running

• University Hospital
  Dresden, Germany Recruiting
• Diakonissen Hospital Dresden
  Dresden, Germany Recruiting
• University Hospital Halle (Saale)
  Halle, Germany Recruiting
• University Hospital Leipzig
  Leipzig, Germany Recruiting

Study contacts

• Principal investigator: Moritz Herzog, MD — University Hospital Dresden
• Study coordinator: Moritz Herzog, MD
• Email: moritz.herzog@ukdd.de
• Phone: 0049 351 458 11501

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.