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

Официальное название

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

Обзор

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

Вмешательства

• Устройство 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.
• Устройство 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

Первичные конечные точки

• Performance analysis of the trained model [Срок оценки: After study completion, estimated 1 year]

Критерии участия

Критерии включения

• scheduled for an ultrasound investigation by an independent physician
• signed declaration of consent

Критерии исключения

• 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)

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Нерандомизированное

Модель

Параллельные группы

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

Германия · 4 центра

• University Hospital — Dresden
• Diakonissen Hospital Dresden — Dresden
• University Hospital Halle (Saale) — Halle
• University Hospital Leipzig — Leipzig

Идентификаторы

NCT: NCT06317181 · DifLivSPECTRUS

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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