Observational and Prospective Study of Hepatic Steatosis and Related Risk Factors Using Ultrasound and Artificial Intelligence

Study Overview
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Brief Summary
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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

Detailed Description
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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.

Official Title
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Observational and Prospective Study of Hepatic Steatosis and Related Risk Factors Using Ultrasound and Artificial Intelligence

Conditions
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Liver Steatoses

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age between 18-70 years MRI regardless of clinical indications, written informed consent Exclusion Criteria: cirrhosis hepatocellular carcinoma or any liver tumours, absence of the right kidney previous liver transplantation large liver cysts or kidney cysts

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 70 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Hepato-renal index calculation | Calculation of the Hepatorenal Index manually and automatically using the AI-based algorithm. | 4 months | 
| Magnetic Resonance scanning and fat percentage evaluation | Proton Density Fat Fraction MRI scans (MRI-PDFF) to evaluate the liver fat percentage as the average value of percentage of fat evaluated for each liver segment | 4 months |