Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT06103175 |
| Other study ID # |
AI-steatosis |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
January 15, 2023 |
| Est. completion date |
November 1, 2024 |
Study information
| Verified date |
November 2023 |
| Source |
University of Bari |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Fatty liver is the most frequent chronic liver disease worldwide and ultrasonography is
widely employed for diagnosis. The accuracy of this technique, however, is strongly
operator-dependent. Few information is available, so far, on the possible use of algorithms
based on Artificial Intelligence (AI) to ameliorate the diagnostic accuracy of
ultrasonography in diagnosing fatty liver. This study showed that the use of AI is able to
improve the diagnostic accuracy of ultrasonography in the diagnosis of fatty liver
Description:
In recent years, ultrasound has taken on a predominant role in the evaluation of liver
steatosis, as it is a non-invasive, non-irradiating method that is easily reproducible and
inexpensive. Of particular effectiveness is the use of the hepatorenal index, evaluated as
the intensity ratio (echogenicity) between the hepatic parenchyma and the renal cortical
parenchyma. The main limitations of detecting the hepato-renal index during abdominal
ultrasound, however, are operator dependence and the use of a relatively long time span to
complete the sequence of operations and calculations required to determine the index itself.
The use of Artificial Intelligence (AI) techniques for image analysis in the medical field is
yielding excellent results. AI-based algorithms are increasingly a powerful tool that allows
the physician to improve their performance in terms of speed and accuracy of clinical
evaluations. Today, there is already evidence of the effectiveness of using AI on ultrasound
images for clinical evaluations. The use of AI as an aid in diagnosing liver diseases through
ultrasound is still under-researched. The hypothesis to be tested is the utility that AI can
have in the evaluation, its general and specific uses in reducing calculation times of the
hepatorenal index.
In this study, 134 patients were enrolled with no clinical suspicion of liver steatosis. All
patients underwent abdominal ultrasonography (US) and magnetic resonance imaging fat fraction
(MRI-PDFF), assumed as reference technique to evaluate the grade of steatosis. The
hepatorenal index (US) was manually calculated (HRIM) by 4 skilled operators. An automatic
hepatorenal index calculation (HRIA) was also obtained by an algorithm. The accuracy of HRIA
to discriminate different grades of fatty liver was evaluated by Receiver operating
characteristic (ROC) analysis using MRI-PDFF cut-offs.
