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

NCT ID: NCT06103175

Last Updated: 2023-11-09

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 150 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-01-15
• Study Completion Date: 2024-11-01

Brief 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

Detailed 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.

Conditions

Liver Steatoses

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: CROSS_SECTIONAL

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Eurisko Technology srl

UNKNOWN

Sponsor Role: collaborator

Centro Radiologico Lucano

UNKNOWN

Sponsor Role: collaborator

University of Bari

OTHER

Sponsor Role: lead

Responsible Party

piero portincasa

Professor, MD

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Department of Department of Precision and Regenerative Medicine and Ionian Area (DiMePre-J - Clinica medica "A. Murri"

Bari, BA, Italy

Countries

Italy

Other Identifiers

AI-steatosis

Identifier Type: -

Identifier Source: org_study_id